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    "## 数据集\n",
    "\n",
    "对于课程教程，我们将使用一系列的数据集，以更好地展示算法的优势和劣势。这些数据集包括以下内容：\n",
    "\n",
    "* Fisher's Iris: 每个项目代表一朵花，有四个尺寸：萼片和花瓣的长度和宽度。每个项目/花都被归入三个物种之一。Setosa、Versicolor和Virginica。\n",
    "\n",
    "* Zoo: 该数据集持有不同的动物和它们的分类，如 \"哺乳动物\"、\"鱼类 \"等。我们要分类的新动物有以下测量值。1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 4, 1, 0, 1（不要关心这些测量值是什么意思）。"
   ]
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    "### 介绍\n",
    "\n",
    "我们将使用的许多数据集是.csv文件（尽管也支持其他格式）。你可以在网上找到很多数据集，一个很好的数据集库是[UCI机器学习库]（https://archive.ics.uci.edu/ml/datasets.html）。\n",
    "\n",
    "在这样的文件中，每一行都对应着一个项目/测量。一行中的每个单独的值代表一个*特征，通常还有一个值表示项目的*类别。\n",
    "\n",
    "你可以在这里找到该数据集的代码："
   ]
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       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">class</span> <span class=\"nc\">DataSet</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    A data set for a machine learning problem. It has the following fields:</span>\n",
       "\n",
       "<span class=\"sd\">    d.examples   A list of examples. Each one is a list of attribute values.</span>\n",
       "<span class=\"sd\">    d.attrs      A list of integers to index into an example, so example[attr]</span>\n",
       "<span class=\"sd\">                 gives a value. Normally the same as range(len(d.examples[0])).</span>\n",
       "<span class=\"sd\">    d.attr_names Optional list of mnemonic names for corresponding attrs.</span>\n",
       "<span class=\"sd\">    d.target     The attribute that a learning algorithm will try to predict.</span>\n",
       "<span class=\"sd\">                 By default the final attribute.</span>\n",
       "<span class=\"sd\">    d.inputs     The list of attrs without the target.</span>\n",
       "<span class=\"sd\">    d.values     A list of lists: each sublist is the set of possible</span>\n",
       "<span class=\"sd\">                 values for the corresponding attribute. If initially None,</span>\n",
       "<span class=\"sd\">                 it is computed from the known examples by self.set_problem.</span>\n",
       "<span class=\"sd\">                 If not None, an erroneous value raises ValueError.</span>\n",
       "<span class=\"sd\">    d.distance   A function from a pair of examples to a non-negative number.</span>\n",
       "<span class=\"sd\">                 Should be symmetric, etc. Defaults to mean_boolean_error</span>\n",
       "<span class=\"sd\">                 since that can handle any field types.</span>\n",
       "<span class=\"sd\">    d.name       Name of the data set (for output display only).</span>\n",
       "<span class=\"sd\">    d.source     URL or other source where the data came from.</span>\n",
       "<span class=\"sd\">    d.exclude    A list of attribute indexes to exclude from d.inputs. Elements</span>\n",
       "<span class=\"sd\">                 of this list can either be integers (attrs) or attr_names.</span>\n",
       "\n",
       "<span class=\"sd\">    Normally, you call the constructor and you&#39;re done; then you just</span>\n",
       "<span class=\"sd\">    access fields like d.examples and d.target and d.inputs.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">examples</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">attrs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">attr_names</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">target</span><span class=\"o\">=-</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">inputs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "                 <span class=\"n\">values</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">distance</span><span class=\"o\">=</span><span class=\"n\">mean_boolean_error</span><span class=\"p\">,</span> <span class=\"n\">name</span><span class=\"o\">=</span><span class=\"s1\">&#39;&#39;</span><span class=\"p\">,</span> <span class=\"n\">source</span><span class=\"o\">=</span><span class=\"s1\">&#39;&#39;</span><span class=\"p\">,</span> <span class=\"n\">exclude</span><span class=\"o\">=</span><span class=\"p\">()):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">        Accepts any of DataSet&#39;s fields. Examples can also be a</span>\n",
       "<span class=\"sd\">        string or file from which to parse examples using parse_csv.</span>\n",
       "<span class=\"sd\">        Optional parameter: exclude, as documented in .set_problem().</span>\n",
       "<span class=\"sd\">        &gt;&gt;&gt; DataSet(examples=&#39;1, 2, 3&#39;)</span>\n",
       "<span class=\"sd\">        &lt;DataSet(): 1 examples, 3 attributes&gt;</span>\n",
       "<span class=\"sd\">        &quot;&quot;&quot;</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">name</span> <span class=\"o\">=</span> <span class=\"n\">name</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">source</span> <span class=\"o\">=</span> <span class=\"n\">source</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">values</span> <span class=\"o\">=</span> <span class=\"n\">values</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">distance</span> <span class=\"o\">=</span> <span class=\"n\">distance</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">got_values_flag</span> <span class=\"o\">=</span> <span class=\"nb\">bool</span><span class=\"p\">(</span><span class=\"n\">values</span><span class=\"p\">)</span>\n",
       "\n",
       "        <span class=\"c1\"># initialize .examples from string or list or data directory</span>\n",
       "        <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">,</span> <span class=\"nb\">str</span><span class=\"p\">):</span>\n",
       "            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span> <span class=\"o\">=</span> <span class=\"n\">parse_csv</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">)</span>\n",
       "        <span class=\"k\">elif</span> <span class=\"n\">examples</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span> <span class=\"o\">=</span> <span class=\"n\">parse_csv</span><span class=\"p\">(</span><span class=\"n\">open_data</span><span class=\"p\">(</span><span class=\"n\">name</span> <span class=\"o\">+</span> <span class=\"s1\">&#39;.csv&#39;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">read</span><span class=\"p\">())</span>\n",
       "        <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span> <span class=\"o\">=</span> <span class=\"n\">examples</span>\n",
       "\n",
       "        <span class=\"c1\"># attrs are the indices of examples, unless otherwise stated.</span>\n",
       "        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"kc\">None</span> <span class=\"ow\">and</span> <span class=\"n\">attrs</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "            <span class=\"n\">attrs</span> <span class=\"o\">=</span> <span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">])))</span>\n",
       "\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attrs</span> <span class=\"o\">=</span> <span class=\"n\">attrs</span>\n",
       "\n",
       "        <span class=\"c1\"># initialize .attr_names from string, list, or by default</span>\n",
       "        <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">attr_names</span><span class=\"p\">,</span> <span class=\"nb\">str</span><span class=\"p\">):</span>\n",
       "            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr_names</span> <span class=\"o\">=</span> <span class=\"n\">attr_names</span><span class=\"o\">.</span><span class=\"n\">split</span><span class=\"p\">()</span>\n",
       "        <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr_names</span> <span class=\"o\">=</span> <span class=\"n\">attr_names</span> <span class=\"ow\">or</span> <span class=\"n\">attrs</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">set_problem</span><span class=\"p\">(</span><span class=\"n\">target</span><span class=\"p\">,</span> <span class=\"n\">inputs</span><span class=\"o\">=</span><span class=\"n\">inputs</span><span class=\"p\">,</span> <span class=\"n\">exclude</span><span class=\"o\">=</span><span class=\"n\">exclude</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">set_problem</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">target</span><span class=\"p\">,</span> <span class=\"n\">inputs</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">exclude</span><span class=\"o\">=</span><span class=\"p\">()):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">        Set (or change) the target and/or inputs.</span>\n",
       "<span class=\"sd\">        This way, one DataSet can be used multiple ways. inputs, if specified,</span>\n",
       "<span class=\"sd\">        is a list of attributes, or specify exclude as a list of attributes</span>\n",
       "<span class=\"sd\">        to not use in inputs. Attributes can be -n .. n, or an attr_name.</span>\n",
       "<span class=\"sd\">        Also computes the list of possible values, if that wasn&#39;t done yet.</span>\n",
       "<span class=\"sd\">        &quot;&quot;&quot;</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">target</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr_num</span><span class=\"p\">(</span><span class=\"n\">target</span><span class=\"p\">)</span>\n",
       "        <span class=\"n\">exclude</span> <span class=\"o\">=</span> <span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"nb\">map</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr_num</span><span class=\"p\">,</span> <span class=\"n\">exclude</span><span class=\"p\">))</span>\n",
       "        <span class=\"k\">if</span> <span class=\"n\">inputs</span><span class=\"p\">:</span>\n",
       "            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">inputs</span> <span class=\"o\">=</span> <span class=\"n\">remove_all</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">target</span><span class=\"p\">,</span> <span class=\"n\">inputs</span><span class=\"p\">)</span>\n",
       "        <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">inputs</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">a</span> <span class=\"k\">for</span> <span class=\"n\">a</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attrs</span> <span class=\"k\">if</span> <span class=\"n\">a</span> <span class=\"o\">!=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">target</span> <span class=\"ow\">and</span> <span class=\"n\">a</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">exclude</span><span class=\"p\">]</span>\n",
       "        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">:</span>\n",
       "            <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">update_values</span><span class=\"p\">()</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">check_me</span><span class=\"p\">()</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">check_me</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Check that my fields make sense.&quot;&quot;&quot;</span>\n",
       "        <span class=\"k\">assert</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr_names</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">)</span>\n",
       "        <span class=\"k\">assert</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">target</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attrs</span>\n",
       "        <span class=\"k\">assert</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">target</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">inputs</span>\n",
       "        <span class=\"k\">assert</span> <span class=\"nb\">set</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">inputs</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">issubset</span><span class=\"p\">(</span><span class=\"nb\">set</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">))</span>\n",
       "        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">got_values_flag</span><span class=\"p\">:</span>\n",
       "            <span class=\"c1\"># only check if values are provided while initializing DataSet</span>\n",
       "            <span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"nb\">map</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">check_example</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">))</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">add_example</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">example</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Add an example to the list of examples, checking it first.&quot;&quot;&quot;</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">check_example</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">)</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">check_example</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">example</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Raise ValueError if example has any invalid values.&quot;&quot;&quot;</span>\n",
       "        <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">:</span>\n",
       "            <span class=\"k\">for</span> <span class=\"n\">a</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">:</span>\n",
       "                <span class=\"k\">if</span> <span class=\"n\">example</span><span class=\"p\">[</span><span class=\"n\">a</span><span class=\"p\">]</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">[</span><span class=\"n\">a</span><span class=\"p\">]:</span>\n",
       "                    <span class=\"k\">raise</span> <span class=\"ne\">ValueError</span><span class=\"p\">(</span><span class=\"s1\">&#39;Bad value </span><span class=\"si\">{}</span><span class=\"s1\"> for attribute </span><span class=\"si\">{}</span><span class=\"s1\"> in </span><span class=\"si\">{}</span><span class=\"s1\">&#39;</span>\n",
       "                                     <span class=\"o\">.</span><span class=\"n\">format</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">[</span><span class=\"n\">a</span><span class=\"p\">],</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr_names</span><span class=\"p\">[</span><span class=\"n\">a</span><span class=\"p\">],</span> <span class=\"n\">example</span><span class=\"p\">))</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">attr_num</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">attr</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Returns the number used for attr, which can be a name, or -n .. n-1.&quot;&quot;&quot;</span>\n",
       "        <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">attr</span><span class=\"p\">,</span> <span class=\"nb\">str</span><span class=\"p\">):</span>\n",
       "            <span class=\"k\">return</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attr_names</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"p\">(</span><span class=\"n\">attr</span><span class=\"p\">)</span>\n",
       "        <span class=\"k\">elif</span> <span class=\"n\">attr</span> <span class=\"o\">&lt;</span> <span class=\"mi\">0</span><span class=\"p\">:</span>\n",
       "            <span class=\"k\">return</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">attr</span>\n",
       "        <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "            <span class=\"k\">return</span> <span class=\"n\">attr</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">update_values</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">values</span> <span class=\"o\">=</span> <span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"nb\">map</span><span class=\"p\">(</span><span class=\"n\">unique</span><span class=\"p\">,</span> <span class=\"nb\">zip</span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">)))</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">sanitize</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">example</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Return a copy of example, with non-input attributes replaced by None.&quot;&quot;&quot;</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">attr_i</span> <span class=\"k\">if</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">inputs</span> <span class=\"k\">else</span> <span class=\"kc\">None</span> <span class=\"k\">for</span> <span class=\"n\">i</span><span class=\"p\">,</span> <span class=\"n\">attr_i</span> <span class=\"ow\">in</span> <span class=\"nb\">enumerate</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">)][:</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">]</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">classes_to_numbers</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">classes</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Converts class names to numbers.&quot;&quot;&quot;</span>\n",
       "        <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">classes</span><span class=\"p\">:</span>\n",
       "            <span class=\"c1\"># if classes were not given, extract them from values</span>\n",
       "            <span class=\"n\">classes</span> <span class=\"o\">=</span> <span class=\"nb\">sorted</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">target</span><span class=\"p\">])</span>\n",
       "        <span class=\"k\">for</span> <span class=\"n\">item</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">:</span>\n",
       "            <span class=\"n\">item</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">target</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">classes</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"p\">(</span><span class=\"n\">item</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">target</span><span class=\"p\">])</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">remove_examples</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">value</span><span class=\"o\">=</span><span class=\"s1\">&#39;&#39;</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Remove examples that contain given value.&quot;&quot;&quot;</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">x</span> <span class=\"k\">for</span> <span class=\"n\">x</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span> <span class=\"k\">if</span> <span class=\"n\">value</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">x</span><span class=\"p\">]</span>\n",
       "        <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">update_values</span><span class=\"p\">()</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">split_values_by_classes</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;Split values into buckets according to their class.&quot;&quot;&quot;</span>\n",
       "        <span class=\"n\">buckets</span> <span class=\"o\">=</span> <span class=\"n\">defaultdict</span><span class=\"p\">(</span><span class=\"k\">lambda</span><span class=\"p\">:</span> <span class=\"p\">[])</span>\n",
       "        <span class=\"n\">target_names</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">target</span><span class=\"p\">]</span>\n",
       "\n",
       "        <span class=\"k\">for</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">:</span>\n",
       "            <span class=\"n\">item</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">a</span> <span class=\"k\">for</span> <span class=\"n\">a</span> <span class=\"ow\">in</span> <span class=\"n\">v</span> <span class=\"k\">if</span> <span class=\"n\">a</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">target_names</span><span class=\"p\">]</span>  <span class=\"c1\"># remove target from item</span>\n",
       "            <span class=\"n\">buckets</span><span class=\"p\">[</span><span class=\"n\">v</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">target</span><span class=\"p\">]]</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">item</span><span class=\"p\">)</span>  <span class=\"c1\"># add item to bucket of its class</span>\n",
       "\n",
       "        <span class=\"k\">return</span> <span class=\"n\">buckets</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">find_means_and_deviations</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">        Finds the means and standard deviations of self.dataset.</span>\n",
       "<span class=\"sd\">        means     : a dictionary for each class/target. Holds a list of the means</span>\n",
       "<span class=\"sd\">                    of the features for the class.</span>\n",
       "<span class=\"sd\">        deviations: a dictionary for each class/target. Holds a list of the sample</span>\n",
       "<span class=\"sd\">                    standard deviations of the features for the class.</span>\n",
       "<span class=\"sd\">        &quot;&quot;&quot;</span>\n",
       "        <span class=\"n\">target_names</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">target</span><span class=\"p\">]</span>\n",
       "        <span class=\"n\">feature_numbers</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">inputs</span><span class=\"p\">)</span>\n",
       "\n",
       "        <span class=\"n\">item_buckets</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">split_values_by_classes</span><span class=\"p\">()</span>\n",
       "\n",
       "        <span class=\"n\">means</span> <span class=\"o\">=</span> <span class=\"n\">defaultdict</span><span class=\"p\">(</span><span class=\"k\">lambda</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">feature_numbers</span><span class=\"p\">)</span>\n",
       "        <span class=\"n\">deviations</span> <span class=\"o\">=</span> <span class=\"n\">defaultdict</span><span class=\"p\">(</span><span class=\"k\">lambda</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">feature_numbers</span><span class=\"p\">)</span>\n",
       "\n",
       "        <span class=\"k\">for</span> <span class=\"n\">t</span> <span class=\"ow\">in</span> <span class=\"n\">target_names</span><span class=\"p\">:</span>\n",
       "            <span class=\"c1\"># find all the item feature values for item in class t</span>\n",
       "            <span class=\"n\">features</span> <span class=\"o\">=</span> <span class=\"p\">[[]</span> <span class=\"k\">for</span> <span class=\"n\">_</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">feature_numbers</span><span class=\"p\">)]</span>\n",
       "            <span class=\"k\">for</span> <span class=\"n\">item</span> <span class=\"ow\">in</span> <span class=\"n\">item_buckets</span><span class=\"p\">[</span><span class=\"n\">t</span><span class=\"p\">]:</span>\n",
       "                <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">feature_numbers</span><span class=\"p\">):</span>\n",
       "                    <span class=\"n\">features</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">item</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">])</span>\n",
       "\n",
       "            <span class=\"c1\"># calculate means and deviations fo the class</span>\n",
       "            <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">feature_numbers</span><span class=\"p\">):</span>\n",
       "                <span class=\"n\">means</span><span class=\"p\">[</span><span class=\"n\">t</span><span class=\"p\">][</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">mean</span><span class=\"p\">(</span><span class=\"n\">features</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">])</span>\n",
       "                <span class=\"n\">deviations</span><span class=\"p\">[</span><span class=\"n\">t</span><span class=\"p\">][</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">stdev</span><span class=\"p\">(</span><span class=\"n\">features</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">])</span>\n",
       "\n",
       "        <span class=\"k\">return</span> <span class=\"n\">means</span><span class=\"p\">,</span> <span class=\"n\">deviations</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"fm\">__repr__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
       "        <span class=\"k\">return</span> <span class=\"s1\">&#39;&lt;DataSet(</span><span class=\"si\">{}</span><span class=\"s1\">): </span><span class=\"si\">{:d}</span><span class=\"s1\"> examples, </span><span class=\"si\">{:d}</span><span class=\"s1\"> attributes&gt;&#39;</span><span class=\"o\">.</span><span class=\"n\">format</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">,</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">),</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">))</span>\n",
       "</pre></div>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(1, '../')\n",
    "from utils.utils import *\n",
    "from utils.dataset4learners import *\n",
    "\n",
    "\n",
    "psource(DataSet)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eaf930d-2803-492d-843a-983f8b3a34fc",
   "metadata": {},
   "source": [
    "### 类属性\n",
    "\n",
    "- **examples**。保存数据集的项目。每个项目都是一个值的列表。\n",
    "\n",
    "- **attrs**: 特征的索引（默认范围是[0,f]，其中*f*是特征的数量）。例如，`item[i]`返回*item*的索引*i*处的特征。\n",
    "\n",
    "- **attrnames**。一个包含属性名称的可选列表。例如，`item[s]`，其中*s*是一个特征名称，返回*item*中名称为*s*的特征。\n",
    "\n",
    "- **target**。学习算法将尝试预测的属性。默认是最后一个属性。\n",
    "\n",
    "- **inputs**: 这是不包含目标的属性列表。\n",
    "\n",
    "- **values**: 一个列表，包含了相应属性/特征的可能值的集合。如果最初是 \"无\"，它将从例子中计算出来（通过函数 \"setproblem\"）。\n",
    "\n",
    "- **distance**: 学习器中使用的距离函数，用于计算两个项目之间的距离。默认为`mean_boolean_error`。\n",
    "\n",
    "- **name**: 数据集的名称。\n",
    "\n",
    "- **source**: 数据集的来源（URL或其他）。在代码中不使用。\n",
    "\n",
    "- **exclude**: 要从`inputs`中排除的索引列表。该列表可以包括属性索引（attrs）或名称（attrnames）。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da4cace9-dc22-4877-8aa6-d45741c7aca6",
   "metadata": {},
   "source": [
    "### 类帮助函数\n",
    "\n",
    "这些函数有助于根据你的需要修改`DataSet`对象。\n",
    "\n",
    "- **sanitize**: 将一个例子作为输入，并在返回时将非输入（目标）属性替换为 \"无\"。对测试很有用。注意：给出的例子本身并没有被改动，而是返回一个经过处理的副本。\n",
    "\n",
    "- **classes_to_numbers**: 将数据集的类名映射为数字。如果没有给出类名，它们将从数据集的值中计算出来。对于返回数值而不是字符串的分类器很有用。\n",
    "\n",
    "- **remove_examples**: 删除包含一个给定值的例子。对于删除缺失值的例子，或者对于删除类（二元分类器需要）都很有用。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba3e68a3-6f61-43e6-9997-bbe667e777af",
   "metadata": {},
   "source": [
    "### 导入数据集\n",
    "\n",
    "数据集可以用以下一行导入。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce6f7469-f531-420e-b13f-5f4e46f988bb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "iris = DataSet(name=\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012561a2-8c35-418a-b415-5c3baa0b6ceb",
   "metadata": {},
   "source": [
    "为了检查我们导入的数据集是否正确，我们可以做以下工作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc583d3e-8e31-44ac-be87-d5a4d43c28ea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1, 3.5, 1.4, 0.2, 'setosa']\n",
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(iris.examples[0])\n",
    "print(iris.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9ce4cb4-a9d8-4ca0-9915-5ea7d8c7d77e",
   "metadata": {},
   "source": [
    "它正确地打印了csv文件的第一行和属性索引的列表。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef33f319-e60a-404d-a562-50427bbe2a13",
   "metadata": {},
   "source": [
    "当导入一个数据集时，我们可以通过将参数`exclude`设置为属性索引或名称来指定排除某个属性（例如，在索引1处）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f0e082a9-ca54-4e48-b17e-3f8f48bf3cc6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "iris2 = DataSet(name=\"iris\",exclude=[1])\n",
    "print(iris2.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4864635a-fc8c-4bf6-839d-556b88c9e2fb",
   "metadata": {},
   "source": [
    "### 属性\n",
    "\n",
    "这里我们展示一下属性。\n",
    "\n",
    "首先，我们将打印数据集中的前三个项目/例子。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "69ed52a4-24b9-4b7d-9f14-4983dd7cc703",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.1, 3.5, 1.4, 0.2, 'setosa'], [4.9, 3.0, 1.4, 0.2, 'setosa'], [4.7, 3.2, 1.3, 0.2, 'setosa']]\n"
     ]
    }
   ],
   "source": [
    "print(iris.examples[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a6a8ea-6c02-4dd2-9c51-8151e4b4969e",
   "metadata": {},
   "source": [
    "然后我们将打印 `attrs`, `attrnames`, `target`, `input`。注意`attrs`是如何持有[0,4]的值的，但是由于第四个属性是目标，`inputs`持有[0,3]的值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2967a184-65e0-4cdc-b6aa-d0e9913219e7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attrs: [0, 1, 2, 3, 4]\n",
      "attrnames (by default same as attrs): [0, 1, 2, 3, 4]\n",
      "target: 4\n",
      "inputs: [0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(\"attrs:\", iris.attrs)\n",
    "print(\"attrnames (by default same as attrs):\", iris.attr_names)\n",
    "print(\"target:\", iris.target)\n",
    "print(\"inputs:\", iris.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ccbc5f-939d-4872-a1f1-efaa16c35af0",
   "metadata": {},
   "source": [
    "现在我们将打印第一个特征/属性的所有可能值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f129643c-a501-4c97-bd3b-54b3d8087c4a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.7, 5.5, 5.0, 4.9, 5.1, 4.6, 5.4, 4.4, 4.8, 4.3, 5.8, 7.0, 7.1, 4.5, 5.9, 5.6, 6.9, 6.5, 6.4, 6.6, 6.0, 6.1, 7.6, 7.4, 7.9, 5.7, 5.3, 5.2, 6.3, 6.7, 6.2, 6.8, 7.3, 7.2, 7.7]\n"
     ]
    }
   ],
   "source": [
    "print(iris.values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f627640-dbfe-4a17-8908-f4cbc52778f7",
   "metadata": {},
   "source": [
    "最后我们将打印数据集的名称和来源。请记住，我们没有为数据集设置来源，所以在这种情况下它是空的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a9fc51f8-688d-4f7b-98b4-eb2278b09abe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: iris\n",
      "source: \n"
     ]
    }
   ],
   "source": [
    "print(\"name:\", iris.name)\n",
    "print(\"source:\", iris.source)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0e3836-ef6a-41a6-81c6-ecce3ce54d0e",
   "metadata": {},
   "source": [
    "上述的一个有用的组合是`dataset.values[dataset.target]`，它返回目标的可能值。对于分类问题，这将返回所有可能的类。让我们来试试："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a8e7638-72ad-4446-8b10-1ddc85132bee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['virginica', 'versicolor', 'setosa']\n"
     ]
    }
   ],
   "source": [
    "print(iris.values[iris.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c435e57f-1164-418e-9728-17031dd92293",
   "metadata": {},
   "source": [
    "### 辅助函数\n",
    "\n",
    "我们现在将看看在这个类中发现的辅助函数。\n",
    "\n",
    "首先我们看一下`sanitize`函数，它将给定例子的非输入值设置为`None`。\n",
    "\n",
    "在这种情况下，我们想隐藏第一个例子的类，所以我们将对其进行消毒处理。\n",
    "\n",
    "注意，这个函数实际上并没有改变给定的例子；它返回一个经过处理的*副本*。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6451c9da-93d1-4c00-b37d-403168493fc3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanitized: [5.1, 3.5, 1.4, 0.2]\n",
      "Original: [5.1, 3.5, 1.4, 0.2, 'setosa']\n"
     ]
    }
   ],
   "source": [
    "print(\"Sanitized:\",iris.sanitize(iris.examples[0]))\n",
    "print(\"Original:\",iris.examples[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2418c071-cab2-46a5-ac1b-739b7f1bae7f",
   "metadata": {},
   "source": [
    "目前 \"iris \"数据集有三个类，Setosa, virginica和versicolor。我们想把它转换成一个二元类数据集（一个有两个类的数据集）。我们要删除的类是 \"virginica\"。为了达到这个目的，我们将利用辅助函数`remove_examples`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b914eeee-4e3c-4b32-8d29-ab8ec2afa0bd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['versicolor', 'setosa']\n"
     ]
    }
   ],
   "source": [
    "iris2 = DataSet(name=\"iris\")\n",
    "\n",
    "iris2.remove_examples(\"virginica\")\n",
    "print(iris2.values[iris2.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ededc31-c6d2-4510-8791-44c80e425cf4",
   "metadata": {},
   "source": [
    "我们也有`classes_to_numbers`。对于模块中的许多分类器（如神经网络），类应该有数字值。通过这个函数，我们将字符串类名映射为数字。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69dbcffa-6304-4e6c-90c6-91f69afe12cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class of first example: setosa\n",
      "Class of first example: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Class of first example:\",iris2.examples[0][iris2.target])\n",
    "iris2.classes_to_numbers()\n",
    "print(\"Class of first example:\",iris2.examples[0][iris2.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76f05da-d851-49bb-a98d-24438cd2edb5",
   "metadata": {},
   "source": [
    "正如你所看到的，\"setosa \"被映射到了0。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e544e16b-62ef-4e07-924f-45e81d76ce73",
   "metadata": {},
   "source": [
    "最后，我们看一下`find_means_and_deviations`。它找出每个类的特征的平均值和标准偏差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32a009e5-25bc-4651-835c-9114f2c9a9f0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setosa feature means: [5.006, 3.418, 1.464, 0.244]\n",
      "Versicolor mean for first feature: 5.936\n",
      "Setosa feature deviations: [0.3524896872134513, 0.38102439795469095, 0.17351115943644546, 0.10720950308167838]\n",
      "Virginica deviation for second feature: 0.32249663817263746\n"
     ]
    }
   ],
   "source": [
    "means, deviations = iris.find_means_and_deviations()\n",
    "\n",
    "print(\"Setosa feature means:\", means[\"setosa\"])\n",
    "print(\"Versicolor mean for first feature:\", means[\"versicolor\"][0])\n",
    "\n",
    "print(\"Setosa feature deviations:\", deviations[\"setosa\"])\n",
    "print(\"Virginica deviation for second feature:\",deviations[\"virginica\"][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceaf8c9f-adef-43ea-a562-8654f273c1ca",
   "metadata": {},
   "source": [
    "## iris的可视化\n",
    "\n",
    "由于我们将在这个笔记本中广泛使用iris数据集，下面我们提供一个可视化工具，帮助理解数据集，从而理解算法的工作原理。\n",
    "\n",
    "我们使用`matplotlib`和`notebook.py`中的`show_iris`函数在三维空间中绘制数据集。该函数接受三个参数，*i*、*j*和*k*，它们是指iris特征，\"萼片长度\"、\"萼片宽度\"、\"瓣片长度 \"和 \"瓣片宽度\"（0到3）。默认情况下，我们显示前三个特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fc7d235b-57a5-4af4-a6ca-1219c29d7210",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def show_iris(i=0, j=1, k=2):\n",
    "    \"\"\"Plots the iris dataset in a 3D plot.\n",
    "    The three axes are given by i, j and k,\n",
    "    which correspond to three of the four iris features.\"\"\"\n",
    "\n",
    "    plt.rcParams.update(plt.rcParamsDefault)\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "    iris = DataSet(name=\"iris\")\n",
    "    buckets = iris.split_values_by_classes()\n",
    "\n",
    "    features = [\"Sepal Length\", \"Sepal Width\", \"Petal Length\", \"Petal Width\"]\n",
    "    f1, f2, f3 = features[i], features[j], features[k]\n",
    "\n",
    "    a_setosa = [v[i] for v in buckets[\"setosa\"]]\n",
    "    b_setosa = [v[j] for v in buckets[\"setosa\"]]\n",
    "    c_setosa = [v[k] for v in buckets[\"setosa\"]]\n",
    "\n",
    "    a_virginica = [v[i] for v in buckets[\"virginica\"]]\n",
    "    b_virginica = [v[j] for v in buckets[\"virginica\"]]\n",
    "    c_virginica = [v[k] for v in buckets[\"virginica\"]]\n",
    "\n",
    "    a_versicolor = [v[i] for v in buckets[\"versicolor\"]]\n",
    "    b_versicolor = [v[j] for v in buckets[\"versicolor\"]]\n",
    "    c_versicolor = [v[k] for v in buckets[\"versicolor\"]]\n",
    "\n",
    "    for c, m, sl, sw, pl in [('b', 's', a_setosa, b_setosa, c_setosa),\n",
    "                             ('g', '^', a_virginica, b_virginica, c_virginica),\n",
    "                             ('r', 'o', a_versicolor, b_versicolor, c_versicolor)]:\n",
    "        ax.scatter(sl, sw, pl, c=c, marker=m)\n",
    "\n",
    "    ax.set_xlabel(f1)\n",
    "    ax.set_ylabel(f2)\n",
    "    ax.set_zlabel(f3)\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1590e14c-cc5e-41b1-9789-c8aa90becb3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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006GkucbGxhgeHubAgQO0trYWZNPONjJR0lrZzIRXzquYG2Yu711bKdba2ppSKTY7O8vAwID6VK38txOb/Z0UmWx2DCUyyMZXTCGWrXzFtFBSvpmw0SwXfcRxcaGTyS5Cusi+WTookUjg9Xrx+XycP3+e8vLygp3HVpqJNq11/PhxGhoasloTiv/0vd31tZVi+/btW1cp1tPTo2oyy8vLRakUu93VXIXCRqXBmXzFlDLkbH3FtjpGJmxGLvoUysJBJ5NdAu043a2atdxuN+Pj4xgMhhSn30JhszRXKBSio6ODeDyeVQOkdk24c5oK05+qo9Eow8PDeL1etVKsrKwsZeMrRNpkJ6KGYiPbVJrZbKa2tlYtW9/KV8zpdKqkIEnStj3csiUX3W4/N+hkcpuR7ThdSN5Aw8PDTExMUFtbSzgcLjiRwMZprqWlJTo7O7NKa2WCNuIpxo1ZzJvdbDbjcDiIx+OcOnVqw0oxpQx5u53jxcZOpbm2c4xMvmJK5NLX10c0GlWr8SKRSMGul5ZctIPCwuEwbW1t3HXXXZjNZp1ctoBOJrcRuViiaCOCixcvsra2xtTUVFHOKz3NJUkSQ0NDTE5OZp3W2mrdYm2cOxX5pFeKBQIBlVwmJiYAUvSWbCvFdutGnwsK4UwM66+xlsDX1tZYW1vD7XYX1FdMa1gpSRKBQED1F9OnUG4OnUxuE7IdpwtJW5Lu7u6UiCAQCBTcJl6BNs213bTWRuvuFgG+0MctLS2ltLSU5uZmtXM8U3OfErlkqhR7OUQmynsodKWUIAjY7XbsdjuNjY1cu3aN6upqDAZDQX3FtFDuAa04r41c9CmUqdDJZIeh7R1RcsubiewDAwPMzs6qtiQKirkxK2mufNNamXCnaCb5QNs53traqjb3eTyelLnuCrkoJbI7WWlVLGhL2YsJWZax2WzU1NSs8xVTqsW24yumRSKRWFcEo41cMk2h1JLLK20KpU4mOwhJkojH41mltfx+Px0dHYiiyOXLl7Hb7Sm/1/aZFAOLi4usrq5uaVefC3biZio2WW3nPaQ392krxbQlsmazmVgsRjweL5qn2E5V0xW7hyO9NHgrX7GRkRH1c1AIZqvUYzYiv5ZsXulTKHUy2QFk2zuiYGZmht7eXpqbmzl06FDGG7NYZBIOh/F4PAiCkHdaKx0v1zRXrshUKeb1epmZmSEcDvPss88WpVIMip/m2snIZLNrkslXTLF+WVxcXOcrpqQetee9WS/LRnglk4tOJkVGuiXKZkQSj8fp7e1laWmJ06dPq/X4mVAMMlHSWkajkfr6+oISCRSfTO5UKCWyyud59OjRdZVi2nRNPlrAnaqZpCPXjV4xhSwvL2fv3r0b+oppyUUp088H2ZBLIpEgFApRVVV1R5OLTiZFhLZ3RGv3kAk+n4/29nasViv33nvvllYehRytqy05PnbsGF6vt2ilu7u1aXE3QNnoN6sUGx8fRxCEbVWKaY9RLOxUZLKdqEGLTL5iKysrrKysqLqWMnp4fn6+oL5i6eSytrZGf38/99xzzx09hVInkyJA6R0ZHR2ltLSUioqKDb8IsiwzOTnJ4OAg+/btY9++fVmXkBYiMgmHw3R0dBCLxbh48SIOh4OVlZWipNC0ZBKJRIjH4+u0oHzXf7khU6WYMn1SWymmVIltVCmmYCcik53wvMqXTNJhMBiorKxUDVLj8TgTExPMzs4yOTm5zlfM5XIVpMdLuY8VoX6jKZR/9md/xpve9CZe97rX5X3MYkEnkwJD2zuytLSEIAgbOvhGo1G6u7vx+XycO3eOioqKrI9TiDTX8vIynZ2dVFVVce7cuRTjvGI84SvrKpqQMgNeuxEWownzTkE2G73WBl6pFFM8xbSVYhtd050gk50g9UKTSTqMRiMlJSXY7XbOnTu3zlesu7t7275i6dAK/ZlMK2VZ5qmnnuLcuXMFe3/FgE4mBUR678hmA6w8Hg+dnZ2UlZVx+fLlnN1T8yETSZIYGRlhfHyco0eP0tTUtG7tYqWLRkdHWV1d5dSpU5SUlKh5a+UGVYZZVVRU5Cw8a3sBirGh7UajR8WpV3kQUWaJZNr0KioqdiTNtRPi+04cR7vJb+Qr5vV6c/YVS0cikdiwakwhl0AgsKEj926BTiYFgBKWKtVa2gFW6Ru+LMuMjo4yOjrKoUOHaGlp2dZNoWz4uW4OSlorGo2qaa10CIKwbvBWvggGg0QiEbXUWSmD1VY1Kd5MHo+Hnp4eVXhWnrJLS0tflqksBYUgK6PRuG7T83g8qt8VQHd3N5WVlVRUVFBWVlbQJ/ydSHMp16kQfU+bYTMBPhtfsbKyMrVwQusrluk4m70XZRZPoQtiCg2dTPLEZpYo6dMQw+EwnZ2dhMNhLly4QFlZ2baPu52n8I3SWukodGSyuLhIZ2cnBoOBQ4cOYbPZMpKV1ptJuYGUjXBsbEx19lXIZaNxxDuVaikGCn3eZrNZvaaSJPH0009TU1PD2toaMzMzJBKJlAFh+RL2TkUMsDMif7aEtZGvmNfrXecr5nK5Ukg8m6oxPTJ5mSPTOF0ttGmupaUlurq6qKqq4uzZs3k3pSlfvmxyx7IsMzw8rKa1GhsbN70RC6WZaD29Tpw4wcjISNYbgCDcGmalFZ49Ho9aypmuDdypBKJgpyrd6uvr2bNnT8ErxZRj7ERZMBS//Dif0uDNfMWmp6dJJBIquQSDwazIJFMWYTdBJ5NtIL13ZKOyPSUy6e/vZ2pqqqDd5Foy2QxKNBSJRDZMa6WjEJVikUiE9vZ2YrGY2vw4Ojqq/l5J0WULrfC8d+/edV3kijYA4PV6cblcd+QkvZ3oAVGOsVmlmNLYZzKZ1jX2bYadjEx2Wy/LRkj3FVNIXPn+ut1uNcOhXGdthBiPx4lEInqa6+WG9HG6m33ZEokE8/PzWK3WgneTZ0Mmbrebjo4OKisrc4qG8k1zeTweOjo6qKioKFqVWHoXeSQSYWlpicHBQfr6+u5IvWWnIpONrsNWlWJ9fX1blsfuVGSylYtEoY5TDF1GS+JNTU309fUBUFJSss5XrKurS30ALSaZtLa2qm7XWvzGb/wGf/3Xf53VGjqZZAmtJUo2Tr9zc3PMzMxgs9m4ePFiwb+Uys2UiUxkWWZkZISxsTGOHDlCU1NTTjfedjd9WZYZHx9neHiYw4cP09zcvM4kr1gbpsVioba2lsHBQS5evEgkEtmW3nK7sZORyVZIrxTLVB6rrWAqLy9/WZhVKkgkEkXzSNNCkiRKSkpoaWlZ5yv2yCOP8Oyzz2I2m/nVX/1VXv/61/PAAw9w6NChgl7nq1evpuiY3d3dPPjgg7zzne/Meg2dTLJALnNHEokEfX19LCwsqKJnsapOMpFJJBKho6ODcDicdVorm3W3QiwWo6urC5/Pxz333IPT6cy47k6V1+aqt2TbI7ATm32x19/ue0gvj81UwaQUV6ysrBS8UkxBsXtMbsdxtHuE1lfsW9/6Fl1dXbzpTW/i1KlTfP3rX+e3f/u3+Yu/+As+/OEPF+wc0q2bPvWpT7F//35e85rXZL2GTiZbIJdxumtra3R0dGA0Grl8+TKLi4u43e6inVt6OsrtdtPZ2UlFRUVeIn+um75iBWO32zftmdkpMkk/RjZ6Sz79LYU8790UmWyF9Oq7cDjM2NgYbrebrq4uJElKmT5ZqFTj7drki4WtSoNjsRhms5n/7//7//jDP/xDwuGwanVfDESjUf7pn/6Jj370ozl9XjqZbACldySbcbqyLDM9PU1/fz+tra3s379f9dcpdL+GFkofS75prUzrZrvpT09P09fXl5UVzG5xDc6kt2TqbylUuexuQaHJRAvFU6y8vJxIJMLp06cJBAIpqUZtpVhFRQU2m21b57JTZFIIo8dCHMfv96eUBVut1i0LIfLBv/3bv7GyssIv/MIv5PR3OplkQC5prVgsRk9PD16vl7Nnz6ZYp2zWAV8IiKJIJBLh2rVrhEKhvHtXFGST5kokEqrDcfr73mzd3WjEuFl/y/j4uKq3KLpZsfBysDrRNu0qIrOiAyipxsXFRYaGhlJceisqKrI2UnylRSbKvJudeqD50pe+xJvf/Oacx3PrZJKGrXpHtFhZWaGjo4OSkhIuX7687mYo9gArWZbp6uqisrKSM2fOFEws3GrTDwQCtLe3YzAYuHz5ck5PScV8Ok4/xnawld4SDod54YUX7kg/sZ0gk41Kg9NTjdpKMSW6zdZIcSfKj2FnI5PNyCQYDO5Yw+LExARPPPEE3/rWt3L+W51MbiLb3hHltePj4wwNDXHw4EFaW1s3vIGKQSaKJUskEqGlpYWjR48W9ObaLM21sLBAV1cXTU1NGw7u2gi7Jc2VC7SboMFgYGVlhYaGhox6y1a2GVvh5RSZbAVtpdj+/fuzqhRTruvLTYDfikzS01zFxJe//GVqamp46KGHcv5bnUxY3zuyWQ17JBKhq6uLQCDAPffcQ3l5+YbrFkMziUQidHZ2EgqFsNvtVFVVFXyDyJTmkiSJwcFBpqenOXHihGobkeu6t0OAL+S6oihuqLf09vbuar3ldkYmW2GzSrH+/v4UO5Kdikx2U5prJ8hEkiS+/OUv84EPfGBbWY5XNJnkOk5XqZZyuVxcvnx5y/RGoTUTpRnQ5XJx5swZrl69WvS5I5A68+TSpUvb/mJr190tG2y+yFZvyaa/5ZUUmWyF9OuqtSNxu90kEgn1XihWU+pOpLmUPSgbzaTYeOKJJ5icnOSXfumXtvX3r1gyUSwNJEnCZDJtSiTaSYS5VEsVKs2ldRrWNgMWK42mTXMpXfRVVVXcfffdeT2pFXsju90EVYj+ljv9GhUjaki3I5mcnGR5eRmXy5XSlKp1PNhupZgWOxGZKLZCu0EzecMb3pBXVP+KJBMlGunr68Nms3Hw4MENXxsKhejo6CAej+fcBFiIzT4ajdLZ2amm1bTNgIXw0MoExYJ+ZGSE0dHRgpQbK+tqv6zF2th2S8VYLv0tSvqmmLiTIpOtYDabUzrGFdJeWFhgcHBQrRRTru12Ru7uhGairRjdCDupmeSDVxSZpPeObJWGWlhYoLu7m9raWo4ePZrzU0q+momS1iovL8+YVivWECtlyNf09PSG3ezbwW4tDd4pbNbf0tvbqzanKdFLoVM3OzW4qtjHSE8/bVQp5vF4mJqaShm5W1FRkdVURCX9tFNkslWaK5cprLcLrxgyydQ7YjQaM272iUSCgYEBZmdnOX78OPX19ds6psFg2NYAq2wHaBUjzbW6ukp3dzdAVrpQLrgTq7mKiXRdoK+vj3A4zOrqas56SzbYKQF+J1JDm23yG3mKeTyejFMRtZVi2vehrFVMZOOscScMxoJXCJmkj9NVPjhRFInFYimv9fv9dHR0qBMB7Xb7to+rdfbN9ku5WVor0/qFIhNtF39jYyOzs7MF75+406u5oHiEJQgCJpMJk8nEwYMHC+onpuDlkubKNWLYqFLM4/GsqxRTBldlk34qBLaq5IKdE+DzxcuaTDYap6vAYDCkRCYzMzP09vbS3Nyccw9FJuRKJl6vl/b29g3TWpnWLwSZxONxent7WV5e5uzZs5jNZmZnZ/NeNx1aMolGo/h8voKaAd5pkUk6tJt9rnpLtv0td6IAn+kY+XxnNqsUm56eRpIkVRtVooJivadsyUTXTG4jJEkiHo9vaomikImymS4tLXH69Ol1DprbhXYs52bEIMsyY2NjjIyM5DQXvhACvN/vp729HZPJpHaz+/3+ovZqrKys0NbWRiwWS0nlKH5NhTjGnYqNPvet9JZs+lteTpFJodJPmQZX+f1+FhYWWFlZ4caNG+p3VPmvEJViCrJ5L3fClEV4GZJJLr0jBoOBSCTC888/j9Vq5d577y2ogZpCYJtt+NFolK6uLvx+f85id74C/Pz8PF1dXbS0tHDw4EF1EyhWlRgkNZmJiQkOHDhAXV2dagaoVOFYLBYqKyuzFkpfTsjls9xOf8tublrM9RjFIixBENSNe3Z2lvvuuw+fz4fX602pFNOmG7dTKaZAj0x2KdItUTYjElmW8Xg8rK6ucvDgwS0db7eLzVJRXq+Xjo4OnE7ntsTu7aa5JEliYGCAmZkZTp06RW1tbcrvi6FtJBIJvF4v4XCYc+fOUV5eTjQaVec2tLa2qqkcRSgNhUIp1vDZpMR2q+dXttjO+Wfb32Kz2YjH48RisaKR9G7UTLZ7DIPBoPavlJeXq5ViSrpRWymmkEuuD0BbNUYq/XC6ZrKD0M4dURr6NkI0GqW7uxuv14vdbmf//v1FO69MG77W2+vQoUPs2bNnW5vIdkqPw+Ew7e3tJBKJDQsMlGtXqCfZYDBIe3s78Xicuro6KioqMpJgeionHA7j8XjweDzMzMwgSdK6lFim87tT01yFOu+N9Jbp6WkikQjPPvtswfzE0rFT0c9OCOOZjmEwGKisrFRdsmOxmKq3pFeKKbNxNru2emSyi5DL3BFI9m50dnZSVlbG8ePHGRoaKur5pW/4SlprbW1tS2+vbNZOr0bbDMvLy3R0dFBTU8OxY8c2/BIr168QG8PS0hKdnZ3U19fnvJbVaqWhoYGGhgY1l+12u1laWmJoaAiLxaISy53k3rsZirERKySt3CPHjx8vmp/YnZ7myvUYJpOJmpoaampqAFLGRff19aVUilVUVOBwOFLWzZZMdM2kyMhl7kim3o2VlZWiDq+CVH+ulZUV2tvbKSsr4957781788s2HaUdnnX06FGampq2XBfyu2m1xzx27BiNjY0MDAxs+3oruWyHw0Fra6uabvB4PIyNjdHT04PD4UCWZXw+H1ar9bZMS8wHOzG2VxCEgvqJZTrGbtno8z3GdqI1i8VCfX099fX1KZViHo9HrRTTEnc8Ht/0ONFolFgspqe5iomNekcyIRwO09nZSTgcThkgVexJiNpjjI2NMTw8zMGDB7ed1sq09laaidK3EgwGsx6epU1zbQexWIzOzk78fn/KMQupxaSnG5QnQp/Px+DgIAMDA1mlxHYbdtroMVu9RUsumz0EvVwik0KYPG5UKaaQy+joKJAkoJmZmYyVYoFAAEAnk2Jgq96RdChplurq6nVz0Ys9CRGSX6jh4WGi0Sjnz5/PK62Vjq3IRBsJXbp0KetISJvmyhVra2u0tbWpA8O0xyxm06LyRDgwMMDZs2fVAoulpSWGh4fVCpzdnBLbqchkM+Tb37ITkcmdVn6sQBtdK55i3d3dxGIx5ufn1WpGpUrMbDbj9/sBiqqZzMzM8Lu/+7t8//vfJxQKcejQIb70pS9x7ty5nNa5o8gkl7SWMn9jampKTbOkQ+kzKZZouLKyoopnly9fxmw2F3T9jchElmWmpqYYGBjgwIEDGw7v2gjaNFcumJ2dpaenh71797J///7bIo4rFXylpaU4HA727NmzLiXW3d1NWVlZiki6W1Jiu82CPpv+FqfTqRK1UgBTTOwmzSQfKJZOSjWjtlJscnKS9773vdhsNurq6vjOd77DAw88UNCHUUhWlN5777088MADfP/736empoaRkZFtHeeOIZNcxukGg0E6OjqQJIlLly5tGCJqJ7cV8ilElmUmJiYYHBzEarXS0tJScCKBzP0g8Xicnp4ePB4P586d25ZBXK6RibbUeLOmz2IZU6Yj/RgbpcQ8Hg/d3d1qlZhCLna7/bakxHZDZLIVttJbEokEk5OTRKPRgviJZcKdkubK9jjK3pP+PX3++ef5yle+wt/8zd/wsY99jKGhIc6dO8enP/1pXvOa1xTk+H/xF39Bc3MzX/7yl9Wftba2bmutXU8muYzTBZibm6Onp4eGhgYOHz68KUkov8umoiJbxGIxurq68Pl8nD9/ntHR0aJtEumbs9LNbjabM86kzxbK0302kYkyOCsej2flZbYTkclWSBdJlTz28vIyIyMjmEwm9Um7oqJix1Jid9pwrEx6i9IAvB29JVtkIpNgLMhXe77K61pfx77yfQU5xk5NWdyItGpra7lw4QL/9E//RF9fHzMzMzz11FMZsyzbxbe//W3e+MY38s53vpMf//jHNDY28hu/8Rv86q/+as5r7WoySR+nu9mTQiKRoK+vj4WFBU6ePLmuES8TtN5ZhYCiUTgcDjWtVUyRX5vmmpubo7u7mz179nDgwIGC+IpttfErXmKVlZUcP358y5tvN1rQp+extamGiYkJtUqsoqKCaDS6I+dTLBSbrJQHvcbGRpxOZ8H8xNKRiUyuzV3j+ennkWSJXzv9a3m/z9sRmWSCMstEEASampp4//vfX9Djj46O8vDDD/PRj36Uj3/847z00kv81m/9FhaLJedj7UoyUbSRcDiM0WjcMhpZW1ujo6MDo9HI5cuXsw6tlebGfDd7Ja01NDS0TqMopsivnHtvby+zs7Pcddddar17vths49e+31y9xHYC+RBWppSYohEsLS2pnm9K1FLIlNhOpLmKDS1hbaa39PX1EYvFUvSWbPtb0ivGgrEgP5r8EYIg0LnUyZB3iEMVh/J6H7tl/nuxpyxKksTdd9/NJz/5SQDOnDlDT08PDz/88J1PJgqRzM7OMjY2xqVLlzbtHVFs01tbW9m/f3/OTxPpzsG5IhaL0d3dzerqKnfffTculyvl98UarQtJfSQQCCAIQt52+enYKM2l1WQyvd+t1twNaa5coNUIxsbG8Pl8uFwu3G73upSYUoGTD+7kyAQ2Lw3OpLco5KLtb1HSYhs9FKZHJtfmrjGxOsGhikMMeYb40cSPOOg6mNd7lSSpKDpnOrKNTIqF+vp6jh07lvKzo0eP8sgjj+S81q4iE60lijK4aqMvRCwWo6enB6/Xy9mzZ9UnyVyRD5msrq7S3t5OaWnphtVaxSKTpaUl+vr6EEWRCxcuFPwpKlOaKxAI0NbWpjoM56rJ7MY0Vy5QZo4o42K1E/3SU2LbqRK7EwT4bI6RzXvW6i1NTU0p/S1KmWwmvUUZNqccQ4lKSswlmAwmmsqaChKd7JY0V7F9ue69914GBgZSfjY4OMiePXtyXmtXkEkmS5SNpiBCUpvo6OhQS27zce3czmYvyzKTk5MMDg6yf/9+9u7du+FNWmjNRJZlhoeHGR8fZ8+ePczNzRUlHE/f+BcXF+ns7KSxsZHDhw9v60a704djZaoS0070i0ajapVYT08P8Xg8pXEym5RYsSOTnSip3a7PXDb9Leklq9qoBKDMUsa0bzrv6GSn0lxbHafYUxY/8pGPcPnyZT75yU/yrne9i5deeokvfOELfOELX8h5rdtOJhv1jhiNRrWCS/va8fFxhoeHt9U/kQm5RiZKWmtlZSWrNI/BYFj3PraLaDRKR0cHoVCIixcvkkgkmJmZKcja6VDSXFryOnHixLZHGCtrajdk5d+FrjC6XTCbzSlpHMVeX0mJGY3GlCqx9Ej2To9M0qOGfLCR3uJ2uwG4cuUKJY4SvrPwHfxhP0OeIbj51qJSlD53H2OrY9uu7NpNkUkx01znz5/n0Ucf5WMf+xh/+qd/yt69e/nsZz/Lz//8z+e81m0lk816R9I3+UgkQldXF4FAoKCd5LmQiZLWKikp4d57780qp1qoNJdSKabY1RuNRnw+X9H0GEEQiMViXL9+nWAwyMWLF/M2m0snk51Iu9wuKI2TpaWl61Jik5OT9Pb2UlpampISU/6uWNgpgb8Y70HRWyoqKlhYWODcuXN4V7yc9J+kPl6PIAs4Shxqs6rVYqXStr3UN+xcY6Qsy1tqJsW2UnnLW97CW97ylrzXuS1kkk3viMFgUAddeb1eOjs7cblc25r7sRmyIZNc0lrpyJdMtMdO9/UqprgvyzK9vb2Ul5fnZMWSzbrFxm7UZTZLiSmVTcr8DKfTqZaDFhI7EZlAceemK993hTTe3/z+FL3F4/Hgm00afc6F54hURLbV37ITaa5s5swHAoGCVWgWG7eFTLSVQhsNsFI+yIGBAaanpzly5AhNTU0Fvxm20jS0aa3tdJTno5nE43F17kqmlFqxNIiZmRlCoRANDQ2cPHmyYNf8TqzmKhbSU2LBYJCuri6CwSDXrl3bMiW2HRRbMylmZKIgUxYjVz+xUkcpZtPm13Mn0lzKvnA7S4MLiduW5tqK9ZUGsaWlpYKkWDY7j42e7ldXV+no6MBms21b6N9un8na2hrt7e1YrdYNj61EJoV64pQkib6+Pubn57Hb7dTU1BRcz9iNUcPthlLZZLFYqK2tpa6uTk2JKdP80lNi23lqLnZkon1ALOYxttrkt/IT+/bct9lbvpefOvRTG/a37KRly2bX606ZZQK7QIDPhIWFBbq7uxEEgbvuuquoFzNTmktrlLhv3768RvpuJxWlGCa2trZy4MCBTSvFlPPN9wYOh8O0tbUhyzKXLl2is7Oz4Bu/lkwCgQCTk5Nq05rWzTlf3MmEpTTSKv0W+/fvJxqNrmv2Ky8vV8kl25TYyyXNlev62v6W3qVePCse/GE/J5dPbtjfslNprpfLlEXYZWSSSCQYGBhgdnaW48eP09fXV/RjppOJNrW0XaNELXIhE21ksJlhonZt5e/yuYHdbjcdHR1UV1erExiLEUUoG9nS0hIdHR04nU7cbjc9PT2UlZVRUVFBZWUlDodj25venZLmyoSNrrfZbKa2tpba2toUc0XFBVmrx7hcrg0j6JdLZLLd9WVZ5kdTPyJOnJgQw+f08aYzb8rY3xKLxVhZWcFmsxXNmy1bMrkTZpnAbSST9C+E3++no6MDURTVbu6hoaGCldVuBK2m4fP5aG9vzyuttdn6m0GZk650s2djCZOvt5i21Dpdk8rW6DFXKDPojx07lpKGUEpop6amEAQBl8tFZWUlFRUVOX8Od3pkstXv080VN0qJuVwuysvL1Q1rJyKT3TxnpN/dT9diFy1lLfiiPn489WMuNV3C5XSt01s6OzuZmZlheHi4IH5i23kvyoODHpnkgJmZGXp7e2lububQoUPqFzJfq5NsYDAYiMViTE5OFiStlWn9rTZl7Zz0I0eOZH1D5jPESonAVlZWMpZaF9ouPh6PMz4+TjQa5dKlSzgcDlUX0856Vypz3G43MzMz9Pf3Y7fbVWIp5M28Hey20t30lFgsFlOjlv7+/hT/q2g0WvCNyR1ysxZdo9XZumNTFrdzDFmWeWryKaKJKA6LgxJzCb3Lvbww/QI/deCn1NcphQ8AZ8+eBSiIn1gmZCPy+/1+XTPJBvF4nN7eXpaWljKmdXaCTCCZ5lleXs7LlmUjbJbmkmWZoaEhJiYmOH78OA0NDTmtvd0hVn6/n7a2NiwWC5cuXcr45F/IyESxYRGE5BjTsrKyDTdObWXOvn37iMViarOacjNv1lW+G9Jc02vTdC9182DrgxjE3Igv3/M3mUwZU2Jer5eVlRV8Ph+BQEC9fvlE37Is853h77AQWOCDZz+4q+e/K1FJk6MJAFEQcVldt6IT661KSa1LuclkKoifWCbomkmBsLa2xtWrV7Fardx7771YrdZ1ryk2mfh8PqamppBlmfvuu68gaa10bJTmikQidHR0EIlEtl2tpoi1uWz6CwsLdHV10dzczMGDBze8MQsVmSj6SFNTE5WVlfT39+f09yaTiZqaGmpqajJ2lWvH8SpPlLczzSXLMs9OPUvXYhd7yvZwtOpoTn9bSKSnxJTKRKPRyMzMDH19fZSUlKjXTpsSywbDK8P0LPUQjAVpX2jnlPPUrp2y+NLcS/hjfqJrUVhL/kxGxigYaV9o54E9D6QcA9ZXnG7XT2wjbEUmCnnpkUkWaGxs3DSllMlSpRDQug0ro0aLQSSQOc2lzAFxuVzr5tLnimzJRJZlBgcHmZyc5OTJk9TV1W36+nwFeFmWGR0dZXR0VI263G53Xmtm6irXjuPt6elBEATm5+cxGo15CfnbxejKaHKDjQd5YeYFDlUcyik6Kfb52u12mpqaUqI+j8fDwMAA0Wg06xSOLMv8ZPonRBNRnBYnz00/x15r9s2828V2yeTepns3NH7cX74/5d/Kw99W7yXX/pb0FO1WZBKNRonH47oAvxXKysq2DAmLEZkoFuput5uzZ88SiUSYmpoq6DG00G72250DshmySUcpnl7hcHjTMca5rrsR4vE4XV1drK6ucuHCBcrKytTfFfLpO332SDgc5tq1a4RCIdrb2wHUjbGysjLvB4atzl2WZV6ae4loIsrBioMMegcZ8AxwrOrYpn+X7fr5Il2AT4/6QqGQqrcoKRxt1Ke9fkpU0uhoxGa0MeAZoGOpA5eY/UiC7b6H7ZDJAdcBDrgOZPVaRRjP9d7MdX5LPB7f0koF0MlkK2TzQRWaTNbW1mhra1NTaxaLhYWFhaKm0pR0UTQapaenh9XV1YJ6i22VjlpdXaWtrQ2n08mlS5eyjoK2G5ko+ogyOljbuV3spkWr1YrRaKS1tRWXy6UK+bOzswwMDGC321ViKYaQr0QljWXJDVZE5MrMFQ5XHM46Orld80wUPUuJXCRJwufz4fF41qXEXC4Xz848mxSzzckUTJm5jCtzV3jA+kDG9QuFnfLMKsQxtprfIssyFouFmZmZjHqL3+9XP5c7AbuimmsjFMpxN32IlrYRsNi6jPKlvHLlilpyXMihO5uluaampujv79+Ww/J2NBOtPqKtylOwEyknrW9ZPkJ+rtBGJcoG21jWqEYnB10HMRk271fYTTPgRVGkvLyc8vLydSmxpzqe4oczP8RqtTIUGcJut2M0GplcmWRYGuYBikcoO9mZXkhk0lu6u7uJxWKq3mKxWNTvo9VqVcuCi/Wd+OM//mP+5E/+JOVntbW1zM/Pb2u9XU0mRqORUCiU1xpKxdhG1VrFJpOFhQUg+SEdOnSoKN5i6WSSSCTo6+tjYWFh2xVquaS5MukjG615u4we01M6wWAQt9udMjFRKT/ejjHghG+CnqUewokwfe5bzba+iI8nx5+ke7Gb+5rvo8GRW8VeIZEPWWmv36pjlcPGwwSCAUKhED6fL/kEbbLjDrkJh8MZC2oKgZ2KTIpdfq7MbHI4HOv0lpGREd71rndRXV1NeXk5Tz31FPfdd19Rrunx48d54okn1H/n8753NZnku9Er/lYWi4XLly9n/DCK5byr3dCBFLffQiL9/EOhkFqGm23zYyZku/Fvpo9sd81iQ/uUuJGQr3TkV1RUUFZWtuVnV2oq5f4992d8fyPeEfo9/TgsDupL6zcVtXdLZLIZLjRe4Fz9OfXfSlXT5OQkq95VXnjhBWw2W0pXfiEb/e7EyGSj4yjXRau3HDx4kOeee46///u/5x//8R/5hV/4BdxuN/fddx+f+cxnuOuuuwp2DkajcctinKzXKsgq20AxNRNZltU871b+VsWITNK72Z955pmizh1RNrDl5WU6Ojqoq6vj6NGjed0Q2aS5NtNHtjrXYmE7m2UmIV8RopXiDKXqb6NNscpexetbX7/u556Qh5GVEcqt5Qx5hzjlP0WjozHncywECklWRlGzdYhQ6aokGo4iJ2ROnjypkvPg4CCRSCRFeM6nym6nGiNv92CsPXv2cP78eZ544gk6OjoYGBjgiSeeKHgf3NDQEA0NDVgsFi5cuMAnP/lJ9u3b3kCx2xqZbLW5bKc0WJvWOnPmjFpZsREUMinUjZZpvG2hR/dqoaw9OjrKyMgIR48epampKe91t0pzbaWPbLRmpkmLhUa+a2o78mVZVoXo2dlZIpEIL7744oZCfiAWoH2hndO1pykxJbusVyIrHHYdZtg7TOdiJw2lDRm/a3dKZLLV+iaTierqarUJWSs8T05OqnY5Wn0gW+xECmqnRvZmO2VREASOHDnCkSNHCnr8Cxcu8I//+I8cOnSIhYUFPvGJT3D58mV6enq2RVovqzSXktZSnpSz+ZIqH2a+XyBJkhgaGmJycnLdeNvt2tBnA0EQVJuSe+65R53YV4h1M51ztvrIRmvuhjRXLhAEQRXylfLZ6urqFCG/vLxc1VsG1ga4Nn8Nu9HOHuceupe7qbZVIwgCdaV1tzU62Qmjx0wPFUqVWGNjY0qj39zcHAMDAykpsfLy8k0rDiVJKprxooLdMrLX7/cXtfv9zW9+s/q/T548yaVLl9i/fz9f+cpX+OhHP5rzei8bMpmenqavr489e/Zw4MCBrL8MyuuysTbYCEo3u+I7lV4XXixdxu/34/P51Jknha4SS9/4c9FHNsJuTHPlAoPBsKGQ3z3YzRXfFaKmKC9EX2C+el6NSgAcZgfz/vkNo5OXS2SyGTI1+ilRy9DQEOFweNOU2MtFgM/mODvtGFxSUsLJkycZGhra1t/v6jRXNqXBW/l7bQVtZLIdeDweOjo6qKio2LCbvRhkMj8/T1dXFxaLhebm5oISCaz/bHLVR7JZs1jYqehnnZA/m8A6ZKVRaKR7ppvro9cpsZWw6l3FZrNhtVhBgFn/LJ6wZ92M8s3Oezm4TJV985RttudcLGynodBoNKakxLSNk5OTkwApQv7LVYDPhJ12DI5EIvT19fGqV71qW3+/qyMTo9G4aWTi9/tpb2/HZDJt6O+1FRR/q1w1Da19++HDh2lubt50iFWhNBNJkhgcHGR6eppTp04xOztbkHXToU1zKfqIVgfa7pp3WporW/ijfno9vTRWNFJtr8ZUbsIb9HLacZpoIMrqyipEwVnupMJWgZXM39VM36Fh7zAfeeIjHK86zscufwynZXupzGKL14VY32az0djYuGFKTBRFYrEYdrt9y5TYdrEbBHhI7m/FjEx+53d+h7e+9a20tLSwuLjIJz7xCXw+Hx/4wAe2td6uJpPNxHHFtj7XtNZmx8kWsViMrq4ufD5fVjpFoTSTSCRCe3s7sViMS5cuUVJSwvz8fFFSaEo0NTIysi19JBPysczP9Rg7jSHPEJ6QR7XsqLHXsBpZxVXp4szxMylCvtvt5qUrL6kd+YpWsNF1+Zfef6FnuYdBzyD3NNzDzx7+2W2d4502zyRTSuz69esAakos1xLubLCTAvxm1ysQCBRMA82E6elp3vve97K8vEx1dTUXL17kypUr7NmzZ1vr7XoykWU55SZIJBL09vayuLi4rbRWJuQSOSgDtOx2e9bpnkKkuVZWVmhra8PlcnHu3Dn1iaxYeowsy6pl+Xb1kXTs1EZfLLLaaN1gLEjPcg+RRISRlRH159FElN7lXg5XHsZusqdsjNqOfGXuiCzLLC4uYjKZ1I78Ye8wPxz7IZIssRpZ5StdX+G1e15LubV8W+ef72cw5UuWSjeXNa/7XbEjH6PRiMlkoqGhgbq6upSUmFLCra0S226PVSKRKLrIL0kSsixvmeZqbCxeocbXvva1gq532zWTzaBc6Hg8jtlsLkhaa6PjZLMhKyJ/rgO08tnwtfPoDx48uK75sRhkEggEVO+gQgr72sikWJvO7YhMjKKRkzUnMxo6GkVjak/GTWTqyL927Ro+n4/Z2VlMJhMVFRV8ZeorzPnnMIpGrAYrPcs9PDXx1Laik/TrHoqFmPHPZG2AGEvE+PATH0aWZb7+M19fZw+z0/NMtCkxWZZVL7aFhQXVDl6rt2SbEtspXQY27zgPBAJ3jC8X3AGRCSQvvJLWamlp2XQOx3aPs1lkokRDS0tL27In2a5mkkgk6OnpYXl5ecN59IUer6udzy7LckGF/XQyiUajGAyGgqcUdlqXMRvM3FWz/a5kRcg3GAwcOnSIkpISVldXuTFxgx+O/pBIJILRYMRmtOGL+fKKTrRk8sjAIzw9+TSfePUnqCvdugv6h+M/TFrFyPD42OM8dOChlN/fzoZCQRAoKyujrKwsxZ7E4/EwMjJCKBRKSYk5HI4N95CdSHNlSyZ3imMw7HIyUcTx/v5+PB5PwdJa6dhssw8EArS3t2MwGLLuXUnHdjSTYDBIW1vblsct1BCr9P4RxRyzkNBOhlTGJBsMBrUBMNcGtt0EWZYzVmjlugagXpMX+l5gjTVMZhM20YYsycgJmbbZNv7hmX/gXSfeldM100Ymi4FFfjD2A6Z8U/xg7Ad84OTmomssEePvOv5OPce/6/g73rD3DSnRyVZpm0IgW8JKt4PP5GqwUUpsJ6q5lGNs9l50MskBW30p/H6/mgLIx2dqK2wUmShTCfOtYso1FZXLTHiluiUfZOofmZ2dLcrkP4De3l48Hg/nzp1DlmW8Xq9araOd915eXp7zNb9dAvyV2St8o+8b/PY9v01LWcu211HO3xf28fzU84TiIWKJGCHhpuGpCAkhwZPLT3Jq8hRd17o4WXmS5prmLaclasnk8bHHWQouUV9azw/Hf8gb975x0+hEiUoqrMnouN/dvy462c1WJ+muBkqVWKaUWD49Z9kim+gnGAzqZFIIzM7O0tPTo4b+xSISWB85aMtvT5w4kbcRWrZpLlmWGRkZYWxsLOvqqXw1k2AwyI0bN9b1jxQq4tEiGo0CySeuixcvqte9vLxcFaWVp8fe3l4SiQQul0sll2y/Azud5oolYny97+v0LPfwxNgT/NJdv7StdbTnXWou5SMXPsJjI48RlaIprxMFkaOVRzFVm+jx9FBuLqch0aAK+eXl5Wq0p7XWV8hEiUpcVhe1JbX0ufs2jU60UYnVmIyCZOR10Umxq8WgMHqGNiXW2tq6LiUWDAaJRCKEw+EtU2LbRTYje++k+e+wC8lE67Z71113MTQ0VPTNQRuZhMNhOjo6UspvC7H+Vht+LBajs7MTv9+fU/VUPmSiREANDQ3rIq9C94Ssrq5y48YNAE6fPo3Val0XUZlMJmpra6mtrUWWZfx+f4qgarPZUqKWnSjfTEemzfJ7I9/jRxM/oqmsieemn+P1e19PqakUg2jIuSdEO4/lDXvfwBv2viHj6xJSgs+89BnmAnNcN13nLSfewuHDhwkGg2r58ejoqCrkV1ZWqpHD46PJqORI5RFEQaTSVrlpdPLD8R/St9yH3WgnFEtGSHajfV10stMCfKGQnhK7cuUKLpeLQCDA9PQ0sizjcrnUtFghRPFsoh+/33/HzH+HXZbmUqq1jEajmtYaHR0t6rwRuEUmbrebjo4OqqqquPvuuwu2WW0VmSgTIEtKSrh8+XJOZYnb2fRlWWZsbIyRkZENI6BCCvtKlLl//34GBwezNoV0OBw4HA716TG9lFa5uZUncOXvdhKxRIyv9nwVf8xPOB5mObTMD0Z/gCiIWI1W3nvsvYjC5u93KbiEQG6fY+dSJ71LvRyrOsb46jjPTj3Lzxz6GbUjv7m5mWgsyqd/8mnuDt2Nb8wHwDPXn+Fb09/CgAF/NDkW1ma0Mb46njE6kWWZv7n+NwDE5XiKI4XZYOaFmRdUMtnNaa5cUV1dTUVFRUpKbHFxkaGhoZQhVtuZfQPZ6TK6ZrJNKBtOc3NzigttoaYtbgZRFHG73YyNjXHkyBGampoKelNspmso73vv3r3s378/5+PmGpnE43G6u7tZWVnZtOGyEGkuWZYZGBhgenqa06dPU1VVxeDg4LbW0tpuaD2xlpeXGR4eVnPesVis6A8fWnxv5Hv0ufuotdfii/ooM5fx/dHvU19ST4WtgtGV0U1Lb+NSnC+2fxFRELlHvierzz8hJXh87HEScoJScykVtgqemniKVzW/KqUA4MfTP+abo99kpnGGzz/4eZ5++mn8Nj+JRIJIOMJ4YByTyYTZbMZldtGx2LHuWD+Z/gkdSx0cqTjC/33T/113fvUltwxNdyIy2YljaDf69JRYptk3DocjpXEym/PLJs2103Yq+eK2k0l6Wqumpibl91tZquSLWCzG8vIysVisoK67WmSKTCRJYmBggJmZmbyq1HIhk430kUzIN80Vi8Xo6OggFAqtSxfmG/Gke2IpOW+3200wGGRoaIilpSV1Rkm2I3llWeaJ8Sc4UX2C+tL6LV+vRCWxRIxScymrkVW8YS+r0VXiUhyHxcGLsy+yr3zfhtFJ+0I7/e5+ACrFSi5wYcvjKlFJU1ly1EBdSR29y71qdAJJwvmHrn/AH/VzZfYKV+euAvCWE2/hviP3IUkS/oAfr9fLineFNf8aFdYKhoaGUoofPnvts4TjYXrdvUz4JnjtntdueF47YdeyU6m0jTb6TLNvFKPKrq4uJElaVyWW6ZpsRSahUAhJkvQ0V7YIhUK8+OKLiKK4YbVWMcfqrq6uqkOsampqimZdkK6ZhMNh2tvbSSQSXL58Oa8cbLZkspk+kgn5pLn8fj83btygpKSEixcvpqQBirHZaHPewWBQnezn8XgYHR3FbDar6bDNmtcGPAN8rfdrnKs7x389+1+3PNefzPyE0ZVRbCYb3rCXuBxneW0ZURAJxUJU2arod/czsjLCQdfBdX8fl+I8PvY48s3/u+67znvk92x6TCUq8Ua8WE1WvGEva9E1+pb7+I+R/1Cjk6cmnqJ7qZumsiYWA4v8Q9c/8A7TOxBFkdqS2uRiDvi69+t0xjr5owf+SN0UlTTiuDzO89PPU2IqIZKI8FdX/4oHWh7YdFJkMTd65eFmN+kyVquV+vp66uvrVZ3P4/GwtLS0aUosm1kmgJ7myvrgRiO1tbXs27dvww+vGGkupYeiv7+f/fv3q1+CYkG74Xu9Xtrb26msrOT48eN56zJbkUk2+shG624nMlGaHpV0ZfrGo0Q8giAUhVi8MS8W2cKx5mM0NzeraQm3283UtWssdnRQ7fFgrarCcvEi5kuXEEwmZFnm8bHHWQgs8NLcSzy48mBGAtBidm2WVmcrLWUtLAWXGFkZocJagUk08brW11FhrWA5tMyLMy+yv3z/uuhEiUqaHE3IyLQvtdO13MXl0ssbHjMmxTAIBk5Wn1R/NuodxR/z4w65CcaClFvK+Yeuf0CSJWxGG5W2Sl6cfZFT1ad4jfAa9e/Womt89MmP4ov6+OlDP835+vNqR34gEOAz3/5M8kFOBoNs4NrsNR7tfJS3HntrRp1gJyITKC6ZyLK87T4Trc63Z8+edSmx7u5utXEyHA5v6ctlMBjuqL6r20omZrOZAwc2t3IodJpL21WudLOPj48XNZWmpLnGx8cZGhri0KFDtLS0FOTG2ywdla0+kuu6mZAtaRXTOTiaiPLjxR9TG63lSGuyUklJS1TF4xh7epCmpgiazUQGBli7cQPv888Tf9vb8Bi9XJ29yn7XfmbWZvjh6A85cPYAAiCMjyMODVE+MEDE6URwOAjXVDK+Ok6rsxVJlpham2IxuIhZNNNc1kwgGmDNuEaZuYwBzwAjKyM0ljYiyRKl5tKUqMRusqvX8MnJJ7nQcgGDmPkhw2q08jsXfkf995BniLaFtuRseQRsRpsalSiW9SWmEtwhN4+7H+dX+VX1b7/Q/gXWomsYBAOffP6TPPpzj6qfUbu3nXZPO3azHYvRQiKRYC26xueufY5yd7m6KVZWVqqls8WOTHaKTGDzzvRskZ4Si0Qiaun70tISsiwTj8czpsQUK5WdKDYoFHZVNVcmGAwGtT8hXygzOUwmU0pXeTFTaQrW1tYIBALcfffduFyugq27UWSSiz6SCbmkuRKJBN3d3Xi93i1Jq5hk0ufuYyY0g1/0rxO+DVeuIE5OwsmTlIoipQBeLxULC0wuLfGt5W8z7Z3G7rTjMDh4fup5Htz7IIcX4hh+/GNkSUIIhbAtLWGIxbC+9rV88NwHiSaitC20MeOfwSya8UV9HKs8hsVoYS26BiSrpWZ8M1yfu05civOfjv8n2hfa6XP3UWYuYzWyCoDdYKfP00fHYgdn685m9Z4fHXyUtegah1yHGPYO852h7/DExBP4Ir6Uvo+YFKMv0EfbYhvnG86zFl3jr176K2RkEnKCJyee5OrcVc7Xnwfgs9c+SygeAiPEosniEVmQGQ4NE2uJ0WBvwOPxqC4JLpeLSCSSdwPtZlAin2JGP8o+UIxN3GKxqCmx/v5+ZFnGbreztLTE8PBwshDC5aK/vx+73b5jKa4///M/5+Mf/zgf/vCH+exnP7vtdW67AL8VCrXRz8/P093dTXNz8zpvr2KO1Q0EAgwPDyNJEvfddx8Wi6Wg62cik1z1kY3WzWbTD4fD3LhxA1EUuXTp0pbvr1hkEk1EeWn2JcwGMzEpxtW5q7eEb1lG7O9HrqwE7bVwubDNzRFNzLNkXuJ403GEhMD48jiRSIS/ffyv+Z2pBspMZmz79hFfWkJyuSAWQ+zowNnyVuImmauzV7EarFxuukzXYhena0+rQriCpeAST008RUJOMOIdYWRlhFJTKQk5gT+adHqQZIkSUwkj3pGsyGTIM8TTk09TbavGIBoos5Tx3eHvste1l1c1v4q2hTb2l++n2l6dLH1fcmMxJj8fJSpRkB6dWAyWjFVooiCCSEo3+Zeuf4nxmXFeb309AwMDTE1NFaUfaKemLMLO6DI2m409e/akpMQWFxf5n//zfzI6OorD4eAP/uAPeOMb38jFixcLPgAP4OrVq3zhC1/g1KlTea9128mkENMWN4O2aurkyZPU1tZmPEYxIpPFxUU6OzupqKggEAgUnEgglUy2q49kQjaRidfrpa2tjZqaGo4dO5Z1/0gxyKTP3cfY6hi1llrMNjP97v5b0YkgIFssCGtrqX8kSSAIPL56jcXoImKJSPdyN5OrkxxwHWBemmQuJBMSWwj29mIymTAajQRdLuzLy+DzcSM0RL+nn+ayZoyikQZDOdfbvstbZkqp2XsC6eBBZFHksdHHiCQiiILIi7Mv8s4j7+T1ra8Hkp/bN/u/STQW5R33voNqR+bKvmHvMG0Lbfzc4Z9DFEQ1Kql3JSvPauw1DHuHeevBtxKviNPn7mOPcw9/+dq/JBaN8fzzz3Oy+mRKVKIgPTr56tu+mtV1Xwou8Y/9/0goHuJUyynuP3E/kJxAOjAwQDQaVTvyKyoqKCkp2XZksVN9LIonYDGRLsBrU2I3btzg//2//8dnPvMZJicnede73oXf7+e5557j9OnTBTsHv9/Pz//8z/PFL36RT3ziE3mvd9vJZCvko5lkWzVVyEmIkNwchoeHGR8f58SJE5jNZnp6egq2vhbKpp+PPrLRuptt+oodf676j3bdQm0MSlRiES2YRBM2o42AFEiJTqSzZzE+8ghyMAh2O8gywtQUclUV5ho7l2UHgWiAJwNPEpNieEIe3tz6aspW7Ryo3UvcaGRqaopoNMrkyAiGWAz/0ACPev8dSZKwGCwwNUXtyBg90jzP9n6J9zx+hMTddzP04N30LvfSUNqAQTDQ5+5j0jfJwYqkwN+52MlTE09hC9j4Zcsvr7N2B5BkiW8NfIvupW4OVxzGaXHy3PRzRBNRhleG1ddFEhEeHXyUhJygxFRCx2IHV2avcLbyrJoi+lLHl/BFfRmv5ade+BSP/OwjWV/7r/V9jeXQMjIy31/4Pg+eeRCXy5Vira/oBNqOfOW/XBr+Xkkje41GI/X19fy///f/kGWZrq4uDh06VNBz+OAHP8hDDz3E61//+lcGmWw3alheXqazs5Pq6mqOHTu26QdXyMgkGo3S2dlJMBjk4sWLOBwOvF5v0TQZJTK5cuVKXvPZM60L6/2WlEhvdnZ2W3b8ypra/58JS8El1epDi2AsqIrYCvrcfYysjFBprcQdcxOLJse6aqOTxPnzCFNTGNraIB4HWUauqiL+0EP82s2nvT997k+RZIm6kjpC8RAHm+7iUMyBODqKce9erFYrFkGg0eVi7eBBnjOvMbo8ijfsZWVxAfv8LCbRiLHUyUtlMm+LVGN74XmulowQcUYosyQtciS/xIuzL7LftR8Bge+NfI+16BqLoUXaFtu4b899665H91I3nYud+GN+fjD6A375rl/mnUfeSVxaH7W/OPsiHYsdHK44zLB3mD957k/44uu/qH6O9aX1nK3NnEY7Unlkk08uFYuBRb7Z/03sRjsm0cRL3pcY8Y1wt+tuILUfSKmsW11dxePxMD4+Tm9vr9rwpxXyN8JOpbl2aspitvbzgiAUJA2lxde+9jVu3LjB1atXC7bmbSeTQqe5tFbqR48epampacu/KZRm4vP5aGtrw+FwcOnSJfWpq5iazOrqKrFYLC99JBMyDbKKRqN0dHQQiUS4dOlS9v0xkgRjYwgjI9TcuIHRZILz51P1Cw2UZkCLwcKvn/l1taRWlmX+8qW/JBAN8Mn7P6n+fDGwSIW1AhmZmBzDkDBgE22YDWaWgkvJVJfNRvzd70Y6dw5hfh4slmQK6qYf01JwiUcGHsEkmnBYHKxF1/hS59/zztf/E+ZIBHFsDIvbjQzI589je9WreE2JlfrGeiLRCF1P/SsvBeZ5HcdxrFkot5QStgnMWMP0TrxExYVXEY6HAai0VSYJ0DtCKB6ibb6Nvc699Ph6+P7o97nYfDFloJYkSzw2+hgxKcb+8v10LnUyvTbN+068b921Wwws8u9D/47L6lKvz435G3zqpU/xnpJkD8u7j76bdx99d3af3SZQopImRxMiIgu+Bb428DXu3nN3xtcr1voVFRUcOHBArW5yu93MzMyoHlgbjSN4JUUmxTR5nJqa4sMf/jCPP/54QUuPbzuZbIVcogZtVJCLWWIhIhNleFemKYzFmIao6CPDw8MIgsDRo0cLur529ogoiqytrXHjxg0cDgcXL17Memodsozw9NOIP/oRRCKsLPZhWhzAOTkJb3kLZIiiupa6GPQMYhAM9Lv71QmGPcs9XJm9QlyKc3XuKhcakt3ir2t9Hfc1JZ/me3p6KHOW0dyUHCtrMVpupdWMRqQjR+DIEV6ceZFIsJdX82oAHr7xMMuhZbWhr9xSzpB3iG8s/Yj3PPQzSNPTrA0OErfZKL/vPjCZMAMnqk8Ql+I8Zfwi3hKJEmcFr4vvJxAMsrbq42pgioAniGdxBo/No+pmJtHEsHeY9sV2IokIDrODanM1vZ5ers9fV98b3IpKmhxNOMwOptem+cHoDzhRfWJd78q3h77NUnCJg66DJKQEM/4Z4nKc745+l9cfeX12n1kW0EYlBiG5KZYaSvnR9I8YcA9wuPLwlmtoq5u0kxKVcQTpxp53qpHkRse5XWRy/fp1FhcXOXfunPqzRCLBM888w+c//3kikci2orNdTybZaiarq6u0tbVRVlaWEhVkA0Uz2Y6FtiRJ9PX1MT8/z5kzZ1Tn0fT1C0kmWn3k9OnTqhtvIaFNcy0sLNDZ2UlraysHDhzI7RpNTSE+8wyy00mwwsF3bTeosq+wr+0aYksLibtTn2JjiRjPTD2DQTCQkBP8ePLHHKk8goDAo4OPEowFERF5dOBRztefRxREREHEZkq6J1gMFqwGq/pvgCfHnwSSpAOwEl7h6amniUtxjlUdQ0bmkYFHQEZ1xQUIx8N8qeNLvOPIOzAeOECYm9U+ad+t9oV2+gQ35Vh5KtLLvfa9VForqHQ6efvKMncdPMds0zFWVlZIRBOIdpHm6mYChgBt821q06JFtBCUg3xv+HucqzuHUTSmRCUOc9Jao8nRROdSJ91L3ZyquZX+CMaCPDH+BJIsMbIykrR2iaxiEk0E40EemXuEn+Pnsv/sNsG3Br/FfGAeg2Ag6Asmr1cijByW+Vrf1/ij+/4op/XSJyXGYjG1I18R8m02G/F4HL/fn5eQvxl2Y5qr0Hjd615HV1dXys9+8Rd/kSNHjvC7v/u7237/t51MspkDv9lGr52RfuDAAVpbW3P+kikXL1cyCYfDtLW1Icvypmkfpcy2EE896f0j2kquQt5cylqjo6NMTk5y8uTJbc11ESYmwO+HPXvoiI4wL64RMMh0laxxur8f7r6bBf8CX+39Kr925P2MvPg9hvu+Qysu4tWV9CY66Hf3I8kSV2avUF9Sj0E00LbYlhKdpJ+3gqXgEtfnryPLMierT1JTUkP7QjuLgUUArs1dw2gwkpAT2E32FA3CYXawFFpizj9Hc1lzxvcXl+I8NfEUoqOM1tqj9C508sLKC7wlcRDB58Oy/zAH3/xODlRWIssy/XP9/J+r/4f7ovfx7OyzjK6NsmJbwWA0EAgHsJvsdC93c2P+Bvc03EPvci+di51EE1EG3APqcT0RD4+PPZ5CJlajlQ+e+yD+mJ9oIsonfvIJysxluKwufGEf7WvtzPvnsxrRuxVOVJ3gF0/9YsrPxsfGaWxq5O76zGmuXGAymaipqUkR8sfHx3G73Vy7di0vIX8z7GSaa7Pj+P3+bemR2cDhcHDixImUn5WUlFBZWbnu57ngtpPJVjAYDMiynHGzjMfj9PT0qFP7Ms1Iz/YYkNsXSbGrz1bgh/xD6Ez9I0pDZ6GfqJRocHZ2NqeU4TrIMggCQTnKc7FhSrBgxshPxBmOJiIAfKnzS3y9918pu9FDaGoEgzWALWGABTdSrcDTrsdZTvgIxoI0O5oRBIHZtdmU6CT1kLc0uBvzN1gJryAj07bQxoWGC7w49yIVtgqMopHrC9f59dO/zpPveXLdECpIdo9X2Db+XimWKC1lLVBuprwkwRPuFS4lyii//36k8+eT/S0kie7F5ReZjEwybh7nwTMPcsR9BH/Aj9/vJ2FMYLfbcZQ6sGNXj//AngeQZZnF4CLLwWWOVh5FEAQq7ambjSiIvKr5VQB8rfdrrEZWaSxtxGQwYTfYmfZN89Wer/LfLvy3bXyQqbiv+T7ua75VKCDLMj8K/YjL92xvtPVmUIR8l8tFLBbj1KlTqk3J+Ph4MrWZoSN/O9ipNNdWkUkwGKSlZfsTO28Hdj2ZKLn5eDyeUqWkzD5RntDz6eHQkslWTziyLDM+Ps7w8HDWdvXKl3O7qS5t/8ixY8dobGzMuHahyCQUCqmps7Nnz26fSAC5uRlsNjq8fUwZvdTIpZQmrIzFp+lqtlDum+K7I99lJejmH4JPcLSikVpjNV5MkDDjXF7k+YEfMGMKU2WvUiOHmpKaDaMTBUvBJToWO1QdpH2hnVAsxGJgkaNVRxEQ6F3u5drcNd60/005vzc1KtGk2er23UVvWS/PHL2Lhw6krjmxOsHzM89TX1pP73Ivb9j7Bn7uaDLtFI1Gee6559i3bx9er5elgSVemHiByspKfrrxp3E4HXz2+mcJJ8K8fu/r2Vu+d8PzCsfD/H3n3xOOh9USYEmSSMgJ/rX/X/n54z9fkOhEi0KXe2eCstGLopiVkK+QSy7kthNpLsX9+HZpJpnw9NNP573GricTZbPU6iZzc3N0d3ezZ88eDhw4UJAxntn0mmi1ivPnz1NeXp7V+vmQyVb9I/kSVTo8Hg9tbW3U19eztraWf/pgzx78F8/xk5c+j4MQ9hWwBsMY66v4F087nseeY8G3wB4qmBBncYkhqnASIAoGEAwGVn2LhGwyvoUlfOEwmM3IFRUIJSU8P/18CploqwOVqORoZbI4oW2hjWHvMK3lrYhCMvXosrq4vnCdu+vvVr2sssWwd5hZ/yzRRJSe5Vt9RAICL86+mDLSFuBHEz9iJbzC8arjDHoHeXzscc7Unkmp3Gpubk4ZJet2uxkcHKTH28Pz3ueRDTI/GPoBv3bu1zb83ofiIZwWJ4crbong8UQcBw5qSmpYi61RR3HIpNjeXJnW30jIn5+fVyd0KsSyVUf+TqS5lH3mdmkmxcJtJ5OtnmQEQVB1E0mS6O/vZ3Z2NuPsk3ywlUju9/tpa2vDYrHk3MuRLVmlIxt/LW3VVb6YnJxkYGCAw4cP09LSwvT0dP7riiLtJ6oYXa2hbC3KuMmNsbqKRQEizNGz1oPVYMUQBltMwCcH+BX5DFW2ckwmI+LKMB5TKatPPo3B60cWDQhSDLk0QPRXf576E+/MeFhtVKKWOSMz5B3CbDCzEvYy55+jz93PoYpDdCx2qAI9JDfHCd8Erc7WDd9aq7OVXzz1i0iy5hr5/Rh6eii5MYW9+4tIp0+TOH+eieAsz0/8mHpPFMN0Gy1WI73RDtoW2lQ/LLj1eWpt9WOJGE89/xSWoAWbZOOxnseoXqvmeMPxjLb6LquLb7z9Gynn6vF4GBwc5OLFi1l/dLlA+Z4UOzLJZr/QCvnaCZ2KkO90OtUqsXQhf6fKj0Enk9sCo9FIIBCgq6sLWZbzngGSCZuVBy8sLNDV1ZXR1ytb5FrRla2/ltLVnI9FiVKRtrCwkKI9FWLaIoAkwLGD9wIwPDxMPB7nRF0dLyy/wFpsjcMVhzHaojROB5gW1vh2pIu3Lx7EHo1SFgjR0jOMeTaIvOcgCEKy3HhmBvn//huhN34g4zH7lvtYjawiCAJLoSUgOXa2xVbP8ckwJ4fm+UJpP0ulbkoFCw2lqdYzj489zp/+5E/59AOf5nJT0hI+/VpYjVbuqrnr1g9WVzF/5W8Ru7uRbTaExAKGa9cQBgf5UcsSKy89Q8NsjARglUGoNfBDwz9x5qfPbHqdO5c6GV4d5lB5K3bBTLc/xqx1lhPCCUZGRgiFQuoGWVlZmbHSqVjmmunr347IZDNkmtC5WUd+NrPZ84US/WxGjHfalEW4Q8gEoKuri7q6Oo4ePVqUDzsTmUiSxNDQUF7VTAqyJZPN9JF8186EaDRKW1sb8XicS5cupQwoK5SP1r3N93Kp8RIDAwPsd+1n7969lNSV8Og3HkUUReYD88njlRmIhhM8bujlNyJ1mGQTKy0tWF96ibDVihgIYDSZMJlMiDU1CAsLGNrbSVy+Nf9DOefDlYdxWp1JU8b+AYTFBWRTHYaOEA0DMwxWiUyVrSFGo8wujTA61c7RqmQ6LCEl+LuOv2NidYIvdnyRS42XsnO4fuEFxO5upEOHkqk4AJ+PwE+eoq+/A1soyFi1LdmsKcsYgkEWX3yCsfuGaSnbq56/FnEpzpOd30Ls7sY5GQBJprnGQe/hGG859hYuHr5IKBTC7XarczOUDVKJWkw357UUM2rYSc1ku0jvyJckSRXyJyYm6OnpwWQyYbPZWFlZyXoEb67YSpdR5sncSVMWYReQyWZfPsXjKhKJsGfPnoI35mmRTiZKt3c4HObSpUt5h5y5ajK5+Gttl0x8Ph83btygvLycc+fOrWtEzGfaohbaEb4Oh4PS0lJWIivUldbhMDsQuPkdKJWpC4dxRkXWzjxI9cEzVMoytv/4DxJWK9Gb4wiCwSBGWcYejeL3+bBk2GRqS2qpCxsx/eX/wtDRAYkEwuoqgtdL6DX38WjjEIvmOKWylbVEmL+78v/jwVM/h9lg5onxJ+hZ7qHKXsW1uWu8MPOCGp1sBkNXF7LdntqIWVZG2UCQj/ZECdbsA+33KBLBuOxhz/deIHrYi5hh1ELn2Av0P/sIsbVVBuyWZGS24sPTM88zVV9j7xs/hs1mo6mpiaamppQNUplRXlZWhtVqVYXfYvVnFNsevtApKK2QD8l5Iz09PcRiMTULsl0hfzNko8voaa4CQtnMlQ2oWCN1FWg3e6UB0ul0cunSpey7vTfBVpYq2c4f8Uf9Kb5UyrnnuunPz8/T1dWVsWNfu26uA7KCsSAl5lvheSAQ4MaNG9hsNi5evKj25RyrPsa/vePfiMViG95YMkAigdzUhLG7G0NtLVabDdluR56eJlJRQZsoEn/uOfWG1xK28Z/+CcPVq0h794LNhtjbizA3x4vzV3nhYIgEMk6sCFKC7uAYj489zpv3vZm/6/g7JFmi0lbJ9Nq0Gp1s+f5NJkRJIv2KCfEE+3wGJGcZGG6lZ4XpFcSJRaTP/zUmp5PTNhuGujokjTNsWVs3rx6Jk6hoAvnW06w4vkS9YwDeqDnO0BCWJ56gbmaG2j17iL/2tYQaG9Wu8lAoxE9+8hN1A62srCxYf0axIx/lGMVMQVksFnXM7p49e/IS8jdDNqm0YDCok0khsLKyQnt7O06nk8uXL6vOv8WEstlPTU3R39+/7QbIjbDZhr+8vExHR8eW+sicf45HBx7lDXvfwIGKW7MmcokgtI7GWxUx5BqZPDn+JI+NPMYfvuoPKbOU4Xa7aW9vp6GhgSNHjqzTd+S1NYyPPYbpyhWIxUjcfTfS0aNJq/hYDLmxETwepOpqxHgcYWQEwWhMppCqqzH+3u9x6cEH1Zt+ZmaG6Ogo+HzM1tXR8vTTyR4Pm41lMcx4dZSzMxb+rXqZRVnGjBEDAo64AZ81mdoSEOhZ7lENJl1WlxqdVJPZGl6BdOYMhvZ2CATgZr5bWFxErqlBCgYR3O5k5AIIHg/CxASy0Yh07BgJoKS3F/OnP03kc59TPcOOzkQ5NeRCSptIKsxakIkSvvlvw09+gvkzn0kew2xGeOYZDI89hvB7v4f1/HmMRiOTk5McOHAAj8fD5OQkvb29lJWVqWJ0WVnZrraHTyQSRZnpoYWSgspXyN/qfWxGJpIk7XhpcCFw28lE+wHIsszk5CSDg4McPHiQPXv2pFRzFROiKDI1NUUwGNy2G+5W66dvzLnqI9fnrtOz1IPT4mSfa5/arJdtBBGPx+ns7MTv96uOxpshF80kFAvxncHv0Ofu4+mJpzljO0N/f/86s011zXAY0+c+h/DCC8mN12DA9PDDYDCQOH06aRMfDCLOzyMdOEDsF38RsbsbcXoaTCaiH/oQ8be8BQGSN73JxKHvf5/I176GaXERQZYRgkG8e/eC2cwjtTN0713FvSrR7oqyJoIJmSARMINgtNKz1MNfvvSXxKU4BsFALBHDLJpxx918seOLfOzgxzbdMBIXLiAODGB48cVbzsSlpSQeeogEYP6Lv0AYG4PSUoTxcQRJQjp8OPle43H8DQ04Z2cx3BwlDEB5ebLx82bzp3odw2Ek5UEgHMb0d38HPl9yvZvXWBwexvSlLxE5c0b1tXK5XLhcLvbv35/SnzE1NYUgCClRSy4b9074Zt1Ob650IV+rU42OjmI0GtXrtlVHfjYNi7Is65rJdqHoBV6vd91o22KTSSgUYmVlBaPRuE6ELhTSNZNc9ZE5/xzt8+3UldTRt9zHqHdUjU6ySXMpaTSLxZL11LZc0lzPTT2nTg/86rWvIlQJ3Hv3vetcCRQyEa5eRbx6lXhrK2JJCfj9CNPTiB4PkiwjHT2K2NGRtGM5cAC5ro5EXR0JQBwYgLTvg/Gb38T013+N0e1Ozm03GBCCQaoGBujc4+R58yJLhjCjTpEL80ZEmxlBFMFoRK6oRK6tIRyPMOOfwSSacIfc6tpmQ3LY1mzTLI32TQjfaiX2i79I4vx5xLExMBpJHD2KfODmgK6yMoxf/zri8DBCaSlSY2NKxCGIIjIgeL3qz+L334/xX/4FcWICqakJRBFheRkMBuJvSjZFisPDyeq2hoZbhCMISPX1iBMTCGNjyKWl64hQ258hSZIa4SmzarT28FuJ0Ts1uGo3uAYLgoDdbsdut28o5Csd+UrEp10zG18uQE9zbQdra2u0t7djtVozdrPnO21xMygpJrPZTH19fVGIBFI1k+3MZ78+d53V6Gqy4c09yIuzL6rRyVZkok035WJTn21kEoqF+O7Qd7GIFmxRG2O+McInwxntbVQyGRpCjseRrdbkzbW6Cjf/Lba3IwSD4PEkhfP5eeQ9e9Q1ZLM5mUpS4Pdj+uIXERYWkAUB2WBAAGS7HSEY5KqvnWizlbKYxLONcX7b9HbeWXEIETCeOoXj1ClcFRUYjAa6lrqIxCPrzttmsmHxZuGyYDIhnTmDdObMul8l7r+fxP33A2D+sz/D8MQTqdcmFkuSgGZCprxnD9H/8T8wf/aziBMTyWjH6ST2C79A4o1vJCso13yTzV4URZxOJ06nk3379hGNRtWoRStGK+XH6ffoy8XRdzsd8JmEfKX8OF3Ir6io2PIYwWAQo9FYlMmsxcRtJ5NIJMKLL76odrNn+sLnM21xI2hTTEePHmV1dbVoM0fgVvSQrT6ihRKV1Jckx7M2OhpTopONyESWZSYmJhgaGsp6tosW2Womz009x6B7EEfUgdFspKmmiSennuSNh96oDoTSrinLMgmzGaNmg5NlGSKR5FO32Zwcs+vzIS4vI8/O3logkUAIBpH371d/ZGhvTz6B3xzPq85JiccZqzTwQm0Mp+TgmitKpNROx6sv8Z9Pvp/V1VXcbjdj4+P09PZu2asxtDKU0/XbDPGHHsJw9Sri8DBSTQ1CJELp3BzSxYskLqWK/YnXvpbQuXMYrl2DaBTp5ElkzWcpHTqE3NyMODaGtH9/MjqRJMS5ORLHjiHv3Ys8P59T5GA2m6mrq6Ouri6lq3x2dpaBgQFKSkrUqMXpdL6iIpOtkKkj3+PxqEK+wWDAbDazvLyMy+VaRyzFdEUuJm47mVitVi5durSp2GS4WRJaKCjagc/nU1NMfr+/qGQiCAJLS0sMDg5m3T+i4PrcdbwRL7UltcSlOBajhVgipkYnmSIISZLo6elheXl5XdowW2ST5grFQnyz65uEfCEaKxpxVbiQZIl+dz9PTzzN2w69TX2tslYwGCR24gRGpxPj4iJyXR2UlSGsrkIkQuLAAeTq6qRe4PEgTE/D8DCC3Y7g8SDt30/irlvNgsLCAhgMSV3h1smDJPHkXpnVihJil06zutQJwH+MPcb9e19Li6sFl8vFgQMHCIfDuN1u3G434+PjGI1GlVjSO8wLAen0aaK/+7uYvvhFxKEhZIOBpdOnqf/9308tH1bgdJJ43evW/xzAbCb2q7+K6dOfRhwYQDYaEeJxpNpaYr/yK3DTLHW7m1Mme3glaunp6SGRSFBSUkIikSAUChUtur8TCUt77RSbnN7eXkKhEIODg0QiEcrLy1OEfIVMioWHH36Yhx9+mPHxcQCOHz/OH/7hH/LmN785r3VvO5lAMjdYyGmLm8Hv96ulqtoUk8FgIBJZn94oBOLxOKurq8Tj8Zzns/ujfkZXRrEarIyvjqs/N4pG5v3zLAQW1kUmkUiEtrY2JEni0qVL266PzybN9Xjn44zMj2C0GZmX5plfvtmAiMDTE0/z1oNvVdeRJImKigpGR0eZicU4eM89NDz7LNaeHoREAiGRAKczeTOvrCDHYkh33QV+P3IigWSxkHjwQaQLF5Kvu3WiJJqbMfb2IkSjyT4PSWKiJMaVJplobRU9yz2IiESlqFok8P6T71ffh9VqpbGxkcbGRjUH7na71Q7z8vJyJEnCZrMVpgw2Hk9qP4EAstGILIpYbvbByDk8aChIXLyI9L/+F8Yf/Qhhdha5uZn4a1+rpgcLWbprMpmora2ltrYWWZbx+/1MTU3h9/u5cuWKOtRKiVoKVc67W9NcuUBJX9ntdvbv358i5A8NDfHbv/3b7Nu3j9LSUpaXl6mu3ryCcDtoamriU5/6FAdu6nVf+cpX+Omf/mna2to4fvz4ttfdFWSyFQolwGsNIg8ePLhuGmIxRH5FH5FlmcbGxpz7ZUpMJfzckZ8jloit+50oiNSW1DIrzqpkovTIuFwuTpw4kdeNkTHN5fEgfvvbCE8/jW9lhT37mvn9d/1XSlrWz/twWpy30lo3Z9I0NDTQ2NiI3+9nqbWVntZWDN3d2CWJ+pISLHY7ZoMhSSwVFVBZiTA+Tvz97ydx/DjSzXEE3Hy4EEURYc8ejEeOkIhGEUZGIBQCgwG/U6S6tJqpuhri/lEqbZXEpTj+mJ8Bz8C681WvqyYHfvDgQfWGn5ycxOfzsbKykhK1GAIBTI8+iuFHPwJJIvHqVxN7+9vB6UyeS2lpSiUWgOGHP8T4jW8gl5cjnTpFPBCgpLcX0+c+R+Qzn0n+bY6Q9+8npkn/pfyuSH0ggiDgcDioqqoiEAhw5swZVS/o6+sjFouljOLNxwbpTklzZXMMs9m8TsiPxWL8+Z//Of/6r//K1atXqaur4+zZs7zxjW/kt37rtwrmRfjWt7415d9/9md/xsMPP8yVK1de/mSSr2YiSRKDg4NMT09v2FtRjDntWn1kuzezIAg0OjZ/UlUik9nZWXp6egrWI7MuzeX3Y/iTP0G4dg0fyQ3q3OIiBKIkPvlJyFBOrUQkykagnJPD4UiWPu7fT/Shh3C73ax88YtYr1zB19JCicORnO0xNwf19RgOHcJgsahd3IrxpyRJRA4fZvnMYSoTUeIOB5bFRSyCwOEzZ/jl3/wQI0Of556yKupKknY4A3Od2AZGMDz9R4iuShKXLyOdO7duw1egdJiHQiEkSaKqqkp18034fJz9+7/H1d8PViuiwYDY1YXxq19Frq9PlvA2NxP/mZ9Jiu+KkeMTTyTTT8p30WLBV9tE6fgU4aevErn/1ohdo1FtW9k2it1UqHy+RqMxZahVIBDA4/GwtLTE0NAQVqtVJZZMekE2xygmdsKCfqNqLpPJxFve8hZVy/vGN77BD3/4Q37wgx8U7X0nEgm+8Y1vEAgEuHRp68bczbAryGSrdEo+kUkkEqG9vZ1YLLapNlPI8uNM/SMDAwNFK28WBIGFhQXW1tY4ffp0wULj9M9F/PGPka9fZ7GsDMNNAVaIxxF6e5GfeALp3e++9cdra8kekqkphJISDBcuQPqwH1lGGBjA1tlJUzgMp05BNEr56Chhr5doIMC0zcbKxYtYlpaokmVKS0sRRTFl4NiaHOV36ts5+3oX7+qsx2I04nzVq5DuuYcnpr7DlG+KmpIapnxTEAxiGBqmKxiifWqeezw2jE8/Tew//Sfi73jH+osQCmF49lnEmRnKEgn899yjRiUHDx4k8fWvYx8YIFBZSVQQMIgipT4flu5upJUVsNsxtrdj/I//IPaf/zPRj30MTCaE5WVkzXcxFof55VJMa2t0/DDA0PKtW7O0VOYtb0nkRSg74c2Vvr4gCJSWllJaWkpLS8s6W/1oNJqiF9jt9k3PsdhkUqhpqFshm9LgkpIS6urqeN/73sf73ve+gp9DV1cXly5dIhwOU1payqOPPsqxY8fyWnNXkMlW2K5msrKyoqZ8MnlPpR+jEJv9Rv0j2qmIhYQyK1uSJC5evLj92nRJSqaIxsdBFJFvVtZpo7XgtWtIfj/WpibKnM6ko5bJBGYzQns7KGQyN4f46U8j9PWBJGEA+N73iP/6ryMpTz+yjOG738Xw6KPJsb6CkCyLPXIE8b3vpcTrpaS8HOuRI8TtdpaXlxkbG0uxZq+srMRgMPD09NMMro4wFjex76f+Ow+cfYD4TXPDcCzMyaqTyU1KAONwB4JbQiivJ9xUj1RThzA3h/Fb3yJx773I9fXq+xUmJrD89/+OYXAQZJmmWIxIUxPCX/818s00qb23F6PRSFlVFZIsk1hbw+jzIQFMTyNIEggCgiRh/vznEWZniXz+80gHD2J47jnk2tpk6a4EQjiaDEOaGykvT5J4OCzg9wvkKxnuBJlstQlrPztt45+iTZnN5pSoJf1+LfZGr3zXdwuZFBOHDx+mvb2dlZUVHnnkET7wgQ/w4x//OC9CuSPIJNc0l3YuvLaTfjMUQjMJBoO0tbWpzY/aOvFipNEU3ytBEGhsbNw+kcTjiI8+ivDssxAOJ6uinE5cBw4gv/GNqjNB1O3mgNWKKT2fH4+rFUiyLCN+/esI3d1IR44gmEzJCGRkBMM//APSiRPgcCBMTWH49rfBYkFubU2uEw4j9vQgnzlD4v1JcdwMNIFqYuj1elleXmZoaIjOzk4qV5f51xt/iC28SNgkMOX+Eo6zD8BNY8NfuuuX1NSYHAxS+ve/AVI1CLVqykmurUUcHETs6SGhkIksY/mjP8Lw0kvJDd5sRjKZsE5MIP7pnxL+yleSFWNms1pFJgoChlgM4Wa3uhCNItlsJAQhWdKcSCB8//uEvvEN+KmfwtrVhTg4iFRbi2HFT/XqAit3vZrQsTPY1TtTJhzOnwR2Is2Vy/rpekEikVhX9JBeql3sXhbl/tyJSYu3e5aJ2WxWBfi7776bq1ev8rnPfY6//du/3faau4JMtvoS5hI1JBIJtSQ2l7nw+W72W/WP5GMTnwlLS0t0dHTQ3NxMPB7Pa6MQ2tsRf/SjZImuMj1yfp7y559n7fBheoNBFhYWOP/2t2MeHEReWAAl1+/xJBv17r03qWWsrGC4ehW5tjZJJJB88t6zB2FkBLGnB+niRYTeXlhdRdYKflYrOByIV6+S+OmfXneeoiiqm8vhw4cJt7fzw099gKlTq7SuCHit8NjaU/zMf34b9f/6fUSrNWUSpRSPJ7vMbxYDRKNyMiKQZcwxGX9QJOxNcoez9yUMTz4JSnVYOIw5kSBRWorY34/Y24t04gSJ++7D+J3vwMoKlJcny3JlOdmAKIqIRiMiN8uiRZGEJBH69rd5qbGRlp/5GZqee46SxUUQRNoOvJHYWz6IwVh4/6ndEJlsBoPBoH62QEZbfcUfy2KxFMygUgtlj3klRCbpkGU572rWXUEmW0Ehk61uCCUyMBgMXL58OaeS2O2mubL11yoUmWhn0B8/fpyGhgb6+/vzWlvo6kr+D+0Y4ro6xIEB1q5dY+Xy5aTNjNVKYnISw9e/Dn19ySf7khISb3870n33JTfsWAxBkpJd6loYDMkn9pv5GmGj8xVFssnpeDwQ/V9/xTdbfJgwYDYYqY7JDJTH+Xb4Ovd/+tNE3vpWNa1itVoRS0vh8mUMjz5KxFnF2KSFaFjGuTpF3FjF99pPERw2UFYGvzzyfYRwGLm0NMkugByJYPD7EUwmBL8fgMR99xF/+9sx/vu/w/JyMkoRxWTfiwJJQkgkkJ1ODLEY9TU1mM6exb1nD50HDhCemSFmcPHUcy0cw0aVJCOKhd34d1tkshXSbfW9Xi8dHR1MT08zPDysGlRWVlZSmsEqZjtILxIpFrIhk2KUBCv4+Mc/zpvf/Gaam5tZW1vja1/7Gk8//TSPPfZYXuveEWRiNBpVcWyjD0GZTFhfX8+RI0dyfrrYDpnk4q9VCE1GibrcbnfK8QRByG/tcFjdMBVEIhHCkQiGRIILFy6o+Wvp/e9HvnQJoa0NJAn51Cmko0dJSBJSIoHociEdPYrhJz9BqqhQU0nC3BxyRUVycBQkNYOSEnC7k0Tj8yVf6/EgbWET4nbL/MEfhHjV+NMMXYSGNQPBBIBASdjAfxyI85aVCaxOJ7Ozs/T391NaWkpVVRU1r389lQMDGHsGcC4YMAoJYiVOBi6/H0tzDVJIZnUVhJHhZDd9LHaLTIxGDKFQMsI4eDB5MgYD0f/+34nff38yJSZJSHV1mP/mbzB0dSWvrcGAXFJyc/pigsT996vVbK2trcROn6a3dwZZlhgZmWFyMkFpaSkORylGo4NC3Ka3Q4AvFBSDSoBz584hSZIatUxMTGAwGLI2WdwMOyG+w9blx4FAgL179xbt+AsLC7zvfe9jbm4Op9PJqVOneOyxx3jwwQfzWndXkEk2aS7IzOiyLDMyMsLY2Jj6pL4d5LrZb6aPZEK+kUk4HKatrQ1BENY1IuY7Xlc+fBhu3EhunCYTfr+f+dFRHGYztiNHUoVQQUA+dAhZIYWb5bnap7roz/4MlrExxN7eZMVSKAQWC4l3vENNj8kHDpB4zWsw/p//g7C0BJKUFP6bmpA0PlzpkCSJrq4h5uereW5vArMEi6Wa6yonv9RXnT5+du9e9u7dSzQaxe12s7y8zA23G+Mb30hN81Fmnglgralk7dR9rDYexQkYDDKxGFBSilRTg7iwoDYWitEoSBLxS5dSyqDXAiLxQxfg0IVbn8mZ+6n55XdjGh1S7V0Ev5/Eq19N/KGHUt6TyWSiqqqcsjIPdXWHCIfDBAIBJiZ8RCJLuFwGpqdlwLVtm/idKg0u5vqQ/K6bzeaUBtPV1VWVWHp7e3E4HGrU4nA4crKG3yky2cqbq5hpri996UtFWXdXkMlWUD7g9M0+FoupluoXLlygrKws059nfYxsUmmQ/fyR9PW3SyZKVVpVVRXHjx8vuB4j3303UkcHQk8PPmDV46HRbsd75gzhTZrglH4P5RwEQWA5uMxv9v9PPvTr7+DV3WuIg4PIVVXJNNj587cWSCQQ+vqSDrmKe+9Ny23jP/4jsVOnkhqKBsrAtEDAQHX1MV47+0v81+t/Q9xgQTKYkBMyxngYswX2feE31b9TTDwVr6TV1VVGG7x8e86MyeSnImzGsbhIWZkDsCIIIrFXPYB9rB+pogJhZgYCARJGI7GGBsK/9VvIsRiCIBAIiHzrWxZ8vvQr1ETVe/+D90j/TMkLP0q6/L7hDcny4wwPHjabxL33LnP27D6St6UTcBKLxfD5PIRCy3R0TKo28cpTeC428XdqZAK3yCT9GIW01d+pPpathnz5/f47zjEY7hAyUWaaaMuD19bWaGtro6SkhMuXL+ctyCkf7mY3xXbmsyvYbrXYzMwMvb29m1al5a3HlJUR/8AHmP7mN0m0tdFw4QKWixfxlpUhZyin1na0AynjWh/pf4S2hTa+YDBz8f1/j0HMfNOIN25geOIJ5PJyqK1Nag1ra2AwIA4MIHZ0JG1TbiIQCNDe3k5paSmnT5/kO98R6T36MS5PdNA89QIQRwYkROIPPYRl0YvU2Yl84sQt48eb51peXs7eveW0tBgpLY0iSWusrflYWloiGjUhSeXMPHCZkqF2jFevkqisJGi1YnC5MP7KryAcPaqmXSMRGa83gc0GNtutaxEKCSyHnfje/ysYPvQrWX0MNpuUofHdRFVVLZC0LvH5fOpGqdjEZ/MUvtsF+K2QbdluPrb62UxAzBfKHrBVZHKnzTKBXUIm2XzJteXBSqf33r172b9/f0FuEm0qLdMXdrvz2bXr57LhS5LEwMAAs7OznDlzhqqbk/cyIV8yiUajtA0NET9+nLPvex8Wmy05V2NgACmNADfqaAdYDi7zr33/ilk007nYyY8nf8xrW1+b8ZjC9etJoV25aQQBysrA602mvFZX1dcq4mtDQwMHDx5kfj55zIjZwVff8yiHB77LnqmfYFhbwbYyz4HZKYx//dfJ1NqlS8Q//OHkAKoM8PlMGI0VmEwVuFwyXm8IrzfMjSE3E/ffT0NLC6bhYVz19VQ+9BDy8eOYb75nSZIwGhMIgoDVKmGz3SJXSRIIhbLfXLNJUwqCkGITH4lE1D6NycnJlGq3dO2g2CaJO7V+LsfIxlZfG7XsVGQCm5NJIBDIy3bmdmFXkEk2MBgMxGIxent7mZubK2inN6Sm0tKjnFz1kY3Wz3bDV9I5kUiES5cubfnFymUiYjr8fj/Xr1+nrKxsXWNnetOi1sYkU9XLI/2PsBhYZI9zD1O+Kb7S9RVe0/KajNGJEI2Cy5U0NgyFkvNLDIbkzPfaWuTmpNfX3Nwcvb29HD58OMVCPxoFt1sgEDAzU/Gz2Epey7uf+TAkEkT3HsRYZgSfD8NTTyHv3UviPe9JOb7RmNRHHnvMkNbHYcJkcmA2V/Hudw8wbTZTevfdjPn92FZXqRocpKqqCpfLddM+JGkhYjRKGI2ymsZIEm4yFRuPC4iimNXQpVxgsVhoaGigoaFB1Q4U12PtSN6qqqqib/bFns9eiI1+K1t9JQXm9XpxKoajBYbysLpZBBkIBPTIpJgQBIH+/n5EUcxqg80VygecnopS9JHtVolp18+GTBRX49LSUi5evJiV9fl2IxOlV2WjWTKiKGIeG0v2W/T3I9XWIr32tYiXLq17rRKV2E12DKKBmpKazNGJLCOMjiKbzchOJ8LQEGIkgmwyQTyOkEggHTqEdPgwoyMjTE5Octddd6VEZqEQDA+LrK3dWvZSoA2DZ5bBkkM0cPPcysqQHQ4MTz5J4p3vTCnXdTjgoYcSdHWJWK2yGlVAsut8bm6VqakFXvva8zidTuLxOB6Ph+XlZXp6eojH4zef/muIxxsQBAOiKKjFECYTiKJ889+SmqIVBEH9rmm/S/kUUCiflaIdpFvqT0xMIMsyJSUllJaWFsdSfwcik2JZwyu2+sPDwym2+tphYNt13k5HNqm0nWhaLAbuCDLxeDwEg0GcTifnz58v2hOQNhWl7efIVR/JhGw0k8XFRTo7OzcdFLbR2rmQiXZo1mYVcLbeXlyf+xxiOIxkt2Po7kb8yU9I/NqvkfjZn015rTYqAbCb7CwGFlOiE2F0FOPnP4/Y1weBQHJOSSKRrPi6WUkmVVeDycTQk0+yYLVy/vz5dTdWSQkcOCAhirKq0Z+e9OPolHA4RExGzcZstUIwmEyppX1vSkqSWrjLJavZNkmSGRtbJByGM2fO4HQmD5BuYOj3+29avCwwNhZhaclAZWUJDkcpNpuNcFjAYEhWHpnNtyreJElK+R5oI5ZCbsbplvrt7e1q5aNiqa9slFt5YmWDO12TMZlMlJaWEo/HOXHiBH6/H7fbzcLCAoODg9jtdjUdVl5evu1zyaZirNjVXMXCriCTzUI+ZdOz2+00NDQUNZRWyoPz1Uc2WltJgaS/X1mWGR0dZXR0lJMnT1JXV5fT2rmQiSRJ9Pb2srS0xPnz5ynXNiqmvpDyRx/F6PWSOH06WRIsCIjT0xj++Z9JvOY1anlsOB7mWwPfIi7HmVqbUpdIyAkG3ANcm7vGBedxTJ/4BMLgYHJeR2UlwthYcgxtVRVyWRnU1JCoqSHY1obQ28s9/+W/ZEwprq7CwICIKCatwQDCwQPcHSkhHvMRjTqw2UlGQW43iVe9KmMFVTri8QRzc3MkEkaqq2uxWjOTv2K7nrReh5ERmJsL4nYHmJpKznOx2+3U1dmQJDOiaE7pxNfqTrIsE4/HicViahoxPWrJF0o5bVlZGS0tLSmeWKOjo6onlmqpv417bCdKg3fKfl77+SoDrRStJV9b/a0ik0QiQTAY1COTQiIej9PT04PH4+Huu+9mfHy8qJMQIbnhB4NBenp68tJHMkG5EdLJREtc2y1vzpZMotEobW1tJBKJLYdmyXNzWCYm8JWVEfb5sFqtWCyWpK368DArP+nDf+AuJKsdyWDhXXs+SCCWzDsZTTLK2zAIBg5WHER87gWEkRHkAweSDCDLScF9ZSVZFnzuHLF4nKXFRcpFkYOHD29IANFo8r+SElnV1RdsR7nhfTUXPI9hnLYhRKwIbjdyZWVGa5b1a8aYnZ3FYrFQV1eL1ysCW1ffORzwnvdAPG4H7GrFlcfjYWVlnLa2VcrKyqiurqaqqiqz6/HaGmNjY1RUVGSMWgqxiWq/d9rucq0nVrqTrxK15Lp+MbBT4nimY2Rrq69ELVuRxVbiO6BrJoVCIBCgra0Nk8nE5cuXsVgsTE1NFWza4kZQRt02NjbmpY9kgvbJVPnfoVCIGzdu5E1c2Qjwa2tr3LhxA6fTycmTJzf9QsuyTEIUMdtslAsCQUHA5/ORiMexCgIlS14mf/v/YIhGCRkdXKn8KZ6qfQ8xQ5Kcyspk/vf/jlJbqznHhYUkgWj9umpqEFZWEHw+wpEIy0tLuPx+SlpaiN11F1upCGZz8r8kRL7V/FuMyXv5varHwLBG4jWvIfG2tyGfPLnpOl5vlLW1eUpLHZSWVhAM5rYppt73AhUVTlpbncBewuEwy8vLyVnzaa7HFRUV6ne9vr5eNd7TzmrRlsQq1Uzb+V5utNlrPbG0g8DcbjfDw8NZb5Q7URq8G2aZbGarPzAwsKWtfjYNi4AemWwX2out6AZNTU0cOnRI/QLlOyBrMyj6SDgcpqWlJW9f/0zQlh4bjUY8Hg/t7e3U1tZy9OjRvG6UrSKTXLQYtWKrogL5zBnMTz2F6dChpAgdi8G1a+B2UxIvxWeuxBlf4O1zf0OjYZZHD/8eobCAzycQiQigoQPFal3RRgDkpiaEiQkS0Sjh69epsVqxVFeTeO97U6zgIenFpfjQLS8nl7n5EKfyUzBh5THnu3j377+NkqOJdRYx6TCbwWgMMDm5SllZFVCCx5P8ndOpJSpYWuLme1oPi0Vmo8JCq9W6zmNKcT0OhULIskxVVRVNTU3qd0QbtSjEoqTGYGMRfzNkEzlkcvL1er243W76+/tT0juVlZUps97vNAE+E7bTZ5Juqx8MBtWUWCZb/WwiE7PZXBQjy2JjV5AJJL/sw8PDjI+Pc+LECerTNpNCDq/SQptmKisrK4g+kgnKjSZJElNTU/T393P48GFa0gdGbQMbkYm2iCDTNU1/bUpHu8FA4pd+CXF6GmF4GGQZkywjhMNEaxuYWzuO0ZggKiQIh5c5Pvt9nql9AzO2Y0jS+vSZdPEi8v79tzQTsxkWFwnW1TF1113U7duHsbaW2N13Ix89mvK3Hg986lMmtfVkdRWWlwXcbgGTCRobZYzGpMZuNoPZImxJJAB+/yT33DPDz/7sYSorzcCt0ch2e3JqMCSJ5GMfM2tbX1LgdMKf/3l0Q0JRoO0DcTgc9Pb2Ul9fTzQa5YUXXsBqtarpMKX0OF1rUchlIxF/ow13O2kog8GgbpSHDh0iGAzidrtZXFxkaGgoZdb7TmgmxTZglCQpr01cEARKSkooKSlZZ6s/PDxMOBxOGo6KIn6/n5KSknXvaaOf3wnYFWSSSCS4fv06wWCQixcvZswXGgyGvC2S05HeP9Ld3V3UaYiCIDA4OJizPf5WyEQmSspueXl58yKCcBjZ50NyOpE0VUXCTdv46Gc+g+HZZxGmp5ENBozf+AYxiwv6k64ERqOBuKmeqtV+GsOTDEp78HpDdHaOIctlVFVVJStTHA5if/AHajWXHI3is1iYeNvbaPzgB7E4HBsqFJFIkkCSXebJCq6GBhlRlAGBe+9NUFqajFR8PmHL8emyLDM4OMjQ0BK9vZe4cWM9+ZWXy3zoQ3HKy5MRiXJ8uz01+RYMJn+XHoltduzx8XHGx8c5c+aMarmeSCRU/zBt6bHW9Rg2jlqyKT3Oa0yBZqNU0jvKE3hvby/RaFQ9n0KW0irYiXG6hT5Guq1+MBhkaGgIv9/PtWvXMJlMKdY4RqNRJZNi4M///M/51re+RX9/PzabjcuXL/MXf/EXHD58uCDr7woyEUWR6upqGhoaNnwyKHRkkql/pBADsjaCMmVxdXW14H0y6WSijCreVGgPBjH87d8i/Pu/J40MGxqI/ef/jPyGNyB2dUE8jnT8OFRUkHjb25J/EwhgfPxxxJVQylJGKYIsmhCcdVTaq5BlZU76nPoEW1VVRXV1NeWf/jTS0BCDHR0E6+o4efFi1lqRzZYs4RWEZMWvJKW61YfDyX/PzwtoP0azWUZpU0kkEnR3d+P3+zl58hxPP23FZpNTGuSDQVhZEUgfjGm3y6xPZcuEQtlt0rIsMzAwwMLCAufOnUsptjAYDBlLj+fm5ujv76ekpES9hkpDXcqsli1KjwstkKeL0i+99BJ2u535+XkGBwcpuTnWubKysiANgDsRmRTb6NFut1NSUoLVauXgwYNq1DI2NkZ3dzef+cxnaGlpoaysrCgFDT/+8Y/54Ac/yPnz54nH4/z+7/8+b3jDG+jt7S0Ige0KMhEEgdbW1k1F5EJpJtrUz9GjR1O6qosxDRFuid+CIHD8+PGCN1xqBfi1tTWuX79OeXn5xkK7LGP44z9G/Pa3wW5HsloRR0awfPzjyH/1V0mhXJKgqor4+96XdPtVZpe87nWIX/4q9riDiMGBUYpQGRhnpvQwPZazhELJ69jQ0EBLS736BLu0tERXVxcej4FoVMRmO8SxxmMsLVnxeiEaFbBYZK0ZL2Zz6r8VGAxJMllbE4hEwOtNRgdXr4rIssCnPiWoOkokkpyh/pGPxCktjdLT04MgCJw5cw8GQ/JFdjvrSCIUoqBQxgesra1xzz33pOgN6dCWpioNdUrU0t7eDqB2tivmhZuVHsdiMWKxGIlEoiilx0okW1NTQ3V1NbFYTI1auru7VdsS5Ql8O4Umu0WAzxeKZiKKomrlAsn79sEHH+R73/seAwMDNDQ08KY3vYk3velNvO1tbyvInpE+r+TLX/4yNTU1XL9+nVe/+tV5r78ryAS2rkgq1DyQ7u5uvF5vxtRPMXSZ+fl5urq62Lt3L9PT00V5ulIik8XFRTo6Orb0LBP6+xGffBK5oiLZhQ7IFgtiezvCygqJ++5LzhhZXsb48MNJW/ibs9vj73sf0eFZyr9+FXFlFlkQGbEe4iuVH2NqMRkBOZ0yFkvyszQuL1Pr91PT0sKgqYH/9b9kIpFkWLHXfwOjDE97LxKVrVgscPr0rUZEp1PmD39jDvu1axxZcLJcfgkwYrPBhQsJfL4kkXzoQzEkScDtNmG3S+qMr3AY2ttFwmGR5WWJcDiEyXSYsrIyvvtd+K3filFMJEnyVpl7IpHg5Ml78PlMhMNwc0THljCZTCk2IKurqywvLzMxMUFPTw9lZWVqOszhcKSUHsfjcfr7+4nH4zidzqKWHivrmEwmamtrqa2tTbEtmZmZUWfLKOmfbC31d7LPpNjHyORc7HA4+MhHPkJVVRVms5lPfOITfP/73+dTn/oUr3vd64ri1bV6UwQsVLp915DJVsh3o8/GX6uQZKItKDh16hS1tbXMzc1lH/nMziJeuQJjY1BRgXz+fNIBNwMUIu7o6Niy6VGWZRgcTKa2qqsV45GkFbwsJ3e/F6+BKJBwlAMQ/OYP8Oy5fDNd5GDlf/wpX+gboHR5Ar+xnD7HeaIGG8abmoHRCIZVD8Z/+UfE69chHCZoseCuO46B3+FB41U+dONXqA5MALAkVvP7js/wbfkdSFIAm82ClLDwuuf/nPqv/m+ESJj/IQmsXGnkn9/4JUYb7lXft80GVVXJU7dak8MitZKbJAkIQoJ4fInaWitOZxnBIKyuCkSjxUubeL3wuc+ZcLsTLC+vYjC0UllZyQ9+kDxmeTl8+MOxrAlFgeJ6XF5enmKbsry8zPj4eEp1kdPppL+/n0AgwD333IPZbE5xfM5Ga8kWG6Vl0m1LtGaLHR0dACnmlBtZxN/OPpNCIpvSYIfDwWte8xpe85rX8KlPfaoo5yHLMh/96Ee57777OLHBvpIr7hgyMRqN2+4zydZfq1Aifzwep6urC5/Pl1JQkHUabWQEwxe+kJyjUVKSFMmvXEF697uRX5vqwptIJBgYGADg7rvvVifSZYJaXlpWhsloRIhEbs0MUToBEQgEJCQMmFYXQZYZ/PcBPr1oorxc5o/+KEY0bqDHeBfmlruSOkZydSApRMciEvYv/V/E7p8QdNWzgonE1CzHx5/nTf5/5rdGPoJRuhUVVEpL/N/VX2DF1oxgO0Y4vMSlvm/y9p5PIhtI2qzEZMoDM/zyt9/B7721nVVbkjCdzmRvYzi80WeRIB6PUFNTQl3drVyW9vU3S/s3/Petn68X2TfqSYlGYXk5xurqAtXVZqqqKm4OnZQ31GS2g3TbFG3pcTAYxGg0smfPnpT0VrqIr3wv8olastU00s0WtZb6WnPKdEv9YvexKO9hp9JcG2Gn5r9/6EMforOzk+eee65ga+4aMslm2mKuUcNm+kgmFEKADwaD3LhxA7PZzKVLl1KetLJdX/z+9xFmZ5GPH781i2NqCvF73yNx5oyaH4lEIrS1takEtVnXbMoMknvuSZbpDgwgNzQk62mDweTI2ZJSIlIJggCSwYwjskypGMJmk29ugAI+H0xNCRgMYDav/9zO2fqx9rYTqG7hB8/biUbBZK6lLjDOm9YeRpQSiNwiVRGII/Krvs/yrYpv4nC4+JnH/xkAyWhEkCUMRgEwYE/4+eOD/8Tqr3wYSBJJRQXMzq5/z36/n2jUjMVixuFYH4maTMmqrZUVYZ1GUl4uq30mFouM05msKMskticJLZVkfD4fi4sRGhvttLSsJ/hCazJwq/TY4XDg9XopLy+nuroaj8fD2NgYFotFjVoU25SNSo9zbZjczmafyVI/fbCVQizxeLzg5pTp2Kk01+0ejPWbv/mbfPvb3+aZZ57Zck/MBbuGTLZCrmSylT6y0THyEeDdbjft7e0bTmDMyvZkdRVhaCjZtKf9+4YGhP5+hLExZJcLn8/HjRs3cLlcHDt2jCeffHLDtddZx1utRP/szzB//OMIIyNJsT2RSO6ugohRiiKKYJbCxA0WRJOI3X5rA4zFIBYTMJvllGGIsVjyNWXiMrIYZC5QSjRWgt1uwmgUiMjl1K4uYGR9hGkkwZFoJ5DcZKrWJpAREQUDgnCrD0ZCJtD9PF7vgzdTOZXArZszeY4yKyureDxhTKZ6DIbM1ihOJ3zoQ/GMEYLZjKq9VFcn+0iybVpMNokOUlZ2noqKnTXsU1wVHA4HJ06cQBRFWltbSSQSaiFEb28vsVhMFfGzKT3eqmGyENVW6YOttON4/X4/FosFo9FIZWUlpaWlBdcfd0P5cTEjE1mW+c3f/E0effRRnn766YLPmb+jyCQej2dVMrfd+SPb1UxkWWZycpLBwcFNI6CsyMRgSJJI+nlIUrKiymBgYWGBzs5O9u3bx759+zQvydy4qEQk2jkK8tGjRL72NcTnn0fweBAGBjB885vEoxLizAqiLBO0uogZLLid+9atC0ltJL2SOxSCFdHJajiCJRpFwEw8nkwOlUdW8QgVuGTPOkKJY2BM3M/CQrLDfZADHJM7NRu9ADKYEDAcuRuDwcDg4CCRSOSmpXodpaVNrK0ZmJvzEY8nsFqrkWURUZQJhZK6CqSmsTbyuUxHkiy27iOZnp5mcHCQw4dP8uyzpVn9TaGgjC+oqanh8OHDKfeJwWCgurqa6urqTUuPFa0l14bJQqehtJb6+/fvV4dZ+Xw+JiYmUno4lB6NfLEbRP5gMJiz0Wu2+OAHP8g///M/8+///u84HA7m55OmpE6nc9Pqwmyxa8hkK4JQvixbMXs+80e2QyaKC+/i4uKWmkVW65eWIp88may2EkWE1dUkiYRCyI2NjIgiI52d64T29EFWyrltNBURAIsF6YEHmJ+HRN0k9U+9QHxljV7nEYxGKBEClCbWGGh9Q8ZTjcVuWZzc+rdEp7mRyPGTOLoHiC2VsSrbqWKZEGH+ll/mM/wPJJLpLQVGEnwm+ps8/h/Jz9kS/whf4pcwSRESoglBlhBlCZ/RxfCFX6bKVEl9/WFCoRDBoBtJmuV1rxsikTBiNBo5ePAgIPGXfynh9ye1DS2JOJ0yZnPhNnrF+XlycpIzZ84QjeaorOeJlZUV2traaGlpYd++fZveT5uVHnd0dCDLMpWVlVRXV6fMTd8oagmHw2rZcTFKj5VzdjqdtLS0IEmS2qMxOjpKT08PTqdTLZXerqX+bkhzFTMyefjhhwG4//77U37+5S9/mV/4hV/Ie/1dQyZbQettlenDyFUfyYRcNROtZnHp0qUt2T1bd1/pzW9G/MEPEB99FCGWFKolp5PJ/fuZdLszuguraycS0NYGbW2Iq6uwbx/CPffABlYq8/PwX/6LmbW1g9yz8ju8c/b/UBqYQUBm1VDG9ZPv4WrVm1I2YrM5Wfobj9/yxwIIh+MEgzEOHizF9N8+xsyn/gFHVyc1whIrQjlfFd7HvyTexSK1/C3/hRKSi0Yx8ft8gu/wNqxCkjv/hfdSJSzxp8b/iT3hRwaGxMP8F9Pf4/6jBg0RWHA4yvmbv6kiHL6GyWTEZrOxvNyBIAj8yq/U4XBUrRsIpW1kzBeSJNHf38/y8rI6f2VhIfm7TGL+RgL/drG8vExnZycHDx6k+eaEylyQXnrs8/lYWlratPRY2XQjkQg9PT2Ul5djs9mKVnqsjRq0PRrp5pSjo6OYTCaVWLK11FfSqLtBgC+WZpLvALatcMeQifJFisfj68oHFX3E4/HkNX8kF81kdXWVtrY2XC4XJ06cyOpLmC2ZCCMjEAwiHzyIbDYjGQz4FhcpefFFLv/8z2POYFMviiJLSxB++v/f3nWHN1mu7ztJ996Tlg5aCi3dLEVBkD1a1J/iRBwHEDwMFcTJ8aiAC0QFBXEi4oECosiGskG6S6GlLW3pTNLdNM1+f3/E9yNpkyZtMwrkvi6vc0iTfF/Wd7/v89zPfR+Ew+nDAAgUtnawyikG+fsKbF96VumJ1QkiEQttbSzY2REUh07EZz6JcCzIAFsqxhXWMHh7hcKxSXlfZVNaeREeN04BZ2fl5DghBI2NjeDxBLC19cPSpRx4DfHGqZkr8c4eLlzZbai3CUAjcYdCDPyieAr7kIopnGNwd5Jgb8sENHM8wSaAj4/SZ6umhoXNiiWomfAsElg5aJQ4YltGIuQKNoa6KJiGt0jEQnOzHJcu5SEx0RuRkZHMLo3OY/D518HnC+Hu7s5MkRtKty+Xy5Gfnw+hUIgRI0YwvQfac9HU3Fe+l+pGkr0FjTWOjo42SHlEtSk+aNAgiMVi1NfXM9JjVb8uR0dH5OXlwdHREcP+cWbWlNVCn7cv0uPuejLaLPWLi4shEom6mFNqeh7Gk+4uaMAbC/2GTPRxNNVUJlLtj1C7+t5C3zJXbW0trly5gvDwcISGhvYoEVEvNde5cwCbDRIbC4lYDB6PB9uICHjX1YHk50Pxj1W5KlpbbfHpK82YcvUsxGwvtNj80xEmCkRmFiB24Bk4/msOuFx199uaGuUUua2tsl3D8fFEi+tkCAQAWlhYuVKKoCDlhZsSCZ+vnBq3tgY4HAW4XC7EYjGCg/wxoDYf4b8ehpUTCx7syajESNSwWeCQWz0LAGiHE/7gpMLLkaBZoDwf1j/+jFZWBCyW8jMXWTmhxPtetLYq78DhUI8u5fNIJBIIhWIEBQVh8OBbuy/VmntERASEQiFzUaQWL9RUsbfJeRKJBDk5OWCxWBg+fLiaFZC7u3KORJv818ZG/6FFbbh58yZKSkoQHx/P+D8ZGra2tmrS4+bmZjXXYxsbG7i6uqKjowMODg5GlR7rc19Nflh011JaWgpbW1uNlvqqyjVjgRKsrjkTC5mYAJ0tVQyVz06hi0yoQWBlZSXi4uLg4+PTo+fXOxGxoQGws4OwvR38+nq4/bNSZPF4IAKBxofIZFaw41XBmbSi3S0Ijv/whVDIRo3MB6EZ13FtmAz/+dABAgELbLaSQDo6gNJSFqysWLCzAxITFbCzU3pcdXQAvr4EAQHq22MbG2XPoaFBjrIyPgAWfL0HYNaJFRhxbTtsrORgsYCJig1YR17AG4p1ULDY6G6XTf8mld5yc1EolMN/9vY0eZdF20cghAWRSITmZhGsrJwREGCL7prdDg4OCA4OVjMppBYv1Jywc4+gO3R0dCA7OxuOjo5ad6Z9JQttoPG7VVVVSExM1J6WaWDQ8pKNjQ3q6uoQEBAAZ2dn1NfXMxdqfaTHqrsXQL9dS2+b49os9Wn2CN210Au4sckEgNl6JsbGbUUm9GJviP6IJnS3c5BKpcjLy0N7eztGjRrVq9UDh8OBVKrbwoMMHQrR5cvgA8qSjKOjstPNYoFoqIkrFApYWVmhVdQBBZHBiiMB29oWUglQVcWGt1iGg8ec8W2ePUrLOUrZrw1BUpICNjYAm80CIcpBvtZWJZEIhcp/c7lASIj68VxdgSefbEFmZj5cXV0xePBgOB3aC8/vfoTCxgpyO+V7Q9pFeJFswWXrMTjuqDSLbG1lQfUtoKRByYTLVRIGnU6/coUNFkupxhIKlbuXq1fZIEQOhcIGLJY9ABZ4PDnCNIvOuqCzSWHnHoGrqyuza9FkB0691nx8fBAVFWVSu3BCCAoLC8Hn85GcnGzyVSyVpAcFBTGN/uDgYEZ6XF9fj2vXrkEikcDDw4N5H7VJjzvvWlTnWTpLjw2xWOycPdLQ0MAkJgJASUlJn3PetYG+xu4Cxtrb22/LlEWgH5GJPj9IejHOy8vrc39E2/Nrymlvb29HVlYW7O3tMXr06F5nHuizM5HL5bjm7w8vZ2cENTeDbW8PNDcrI2gTE0FGjGDuq5pBEhMTg+3uQjR1eMKRVwiu40AoiC04Uhbc0YgrnmPh5MqGlRXAZhMoFCxmcrytjcVczC9fVpINvcC/8ooN/vc/idruRLmyy8OQIUGMB5jNkd0AIWiWOID8o/BSKBzggBbM7NiFNGkK7O2Vz0mPpVCgSxlIoVCSm5WVkjgGDFBO3VdXszB/vg04HAJClBJxa2sryGTKx1AL+Lo6zSFWtrYEmloKnXsENBmRz+errba9vb3h7u6O5uZm5ObmYuDAgT0qcRoCCoUCV65cQVtbG4YPH24QOWdP0NTUhJycHISFhWHgwIFqf+ssPW5vbwefz++V9FjTwKShZbudLfXp5yqTyXD16lXI5XK1XoshLPVVM+a1QSgUWnYmpgCLxVLz4jdUPjsFh8NhLtD0A6dOrQMGDOii3dcbVVVgVVbCViqFoJuMd5FIhOzsbLBcXRG+dq1yEv7KFcDODor/+z8oUlJAvdLVJtqhtKmQOzoiP/Jx3Fe7G5GCCnSI5LCW2yGbk4A8x1iw2R1gsx3A4dwaYxGL1XcGqlJfFktJNKoN5KqqKhQVFWHIkCEICAi4dd/mZoYoWP88lsUB2DICL04TbGwAf38CheKWRby1NRASQuDjQ3DzppLcYmMJE/0ulRLIZCwmu8TKipZHWLCysgIhyl0Mmw3Y2SmJ5N//tkFra9fPyMWFYONGiUZCUYVqMqLqarugoABSqRQKhQIBAQEIDAw0KZHIZDLmYjd8+HC9SnGGBFWMRUZG6qwEqEbbdic9puSiS3osk8kgk8mY240lPbayssKQIUMYMqyvr2cs9R0cHBhi6a2lvj7SY2OquYyN24ZM6uvr0dbWBnd3dyQnJxultqkqP2axWEwpbejQoQjUoITSifZ2cNauBfvwYUAoxAA2G46xscDGjbdi/P5BS0sLsrKy4OnpyUwuy4cOxT+e7mrSH9Wac+eVTqFdAprDAxDQUgiZQITjLYEosR6MCDsZxCIxpFJrKBd9nH8sKpQ7EfoUzs4EbLZyxyKVstDWBuzfz4a3NwGXy4Vc3oQXXkjo4jSquOcesP++DBZRgMVmg8UG2EQONoeFm8H3IspdgQ8/lCIwkKC+XlnGsrMj8PEBqqtZWLXKGu7u6pbzAgHQ3EzPqwO+vlI4ORF4eTmAxVKuXIVCQCBgwcdHuSNpbVVa2asu2js6oDFKWBdUV9sODg4oKSmBn58f2tvbcebMGTg7OzNlHFUfKUNDIpEwIpOkpCSj24p0BpfLxZUrV3qtGNMkPa6vr8fNmzfVZkQ0SY+lUikKCgpga2sLZ2dno0mPVS/0qmQYEhKiZqlP3Z+ppb6np6fei1pdSi6ZTAaRSGQhE2NBtT/i7OwMHx8fozXJ6PNKJBJcu3YNDQ0NGD58eK8bnJzPPgNn1y4QV1fAzw9obob72bPg/Oc/kH/+OXM/alM/aNAghISEqF+U/rkq1tbeMieUyymRcGBnpxwhUfWPKoMvyqx8IbQB8tkc2NsQODlZw97eAdbWrH9WeAS1tS2QSKxAiPs/K3xlDohEArS3K8+By2XhrbcokQWDzQ5GXJwIkyerX5Rl8+aB/ctvcKisg4JYgaUA2AoZGh0G4FT4s7ATAsHBBAMHkn96Gyr58P84/mr7Tba1tSE/vwB2diPg5WXbJXukc6nM3r5rPklv/Tup+3N1dTWSk5OZsqpYLGbq7apuvd7e3vDw8DDYvIJIJEJWVhYjvzW2dLUzqqurUVRUhNjYWHjryiXWA6plxfDw8G6lx25ubkz6KSVR1XKYoaXH2j4zbZb6NTU1KCoqgpOTE5OK2Z2lvj6yYKB7j73+jH5DJtq036rzIxUVFb12DtYH9EuYk5MDNputPaVQH/D5YB84AOLiwuxCFC4ukEmlsDl7FoqSEijCw1FaWoqysrJu1WG1tcC//mXFZJATwmHeLxcXgq+/lsDfH1i3Tt0/qrKSheXL2bC3V0AmA8RiAjabBZmMA0IADscTCoUchLCgUBCwWIp/ppjZUPW7YrHohV/ZW6mv7+p1RQICUPnN77j2zKcY0/wXWCwgc8B07B26Cs3wBfQY1Os8j9HRoST2/Px8BAeHwN7eTuVcjA/qbtDU1IThw4er1bJtbW0REBCAgIAAxq2Xz+ejqKgIYrGYubh4e3v3+jtEe3Wenp4YMmSISctqgFJ6XFpaivj4eINlXnSGNulxSUmJmuuxRCKBlZVVl16LroRJfYlF3+n37iz18/LymBKeJkt9feznAVh2JoaAakCWpvkRQ6UtakPzPzUVe3t7xMXF9Wl1yaqrU14NVX6ELBYLcmVEIBRVVcgVCNDS0qIx9766+tZOpLJSqaqysSFwcCCws2MxCifV8k1n/ygOh8DLi6CtjcWUiwIDlX0LJyeCNWukKC9nY8ECZf/CykopuZXJ5FAnE2rXotxFNDZqfs3S4DCsHbQV9rZy2NmzmNqZLodcW1sCFxfyz2u5dbtIJAaL1YphwyLg6Oit8bmM4b4LKEsOeXl5kEgkGDFiRLelDOrW6+np2aXeTleulFj0DYOiQ7EDBgzoNujMGKDWMJWVlUhMTDSoyKU7UOmxquuxj48PE22rj/S4twOTvW3w98RSXyaT6ZQF29nZGX0K31joV2RCoW1+xBhJiBTV1dW4evUqOBwOQkND+/yBEn9/ZbNcIGAyQ1gAOB0dIHZ2yObzIf1HHda5mZqbCyxYYM1YlUilQG0ti5H0jh6tYHoC2nI8AGX5a8sWMUSirhciOzsCf3/lj8jaWhkiJZWyALDRWXCmUKg//vPPrTF7tgIDBqjvEm6RAgcdncpKLi630hc7w88P2Ljx1q6Klja5XC4SEoYgMtILtbXK/Hca1asKZ2fl6zFU2BW1ybG2tkZycnKPehSa6u20jJOVlQU2m81cED09PTU+Nw2OGjRoEIKDgw3ymvQFnaWqq6szi/RYIpEgMzMT9vb2iI2NBZvNZvJYNEmP6XtJlW29HZg0hGOwLkt9QgisrKzA5XLh4eHRRRUqEAg0StFvF/QrMiGEoKysTOv8iKHCq1ShUChw/fp1VFVVISEhAQUFBX2yoWfg5QXFrFlg//ST8t/OzmC3tsKqpQWVo0bBOioK8dHRXVZDXC6wapUVioqU5KGU6RKmLyCRKA0L9VWFKi251C/imZksNDezcfWqcigwLEyO9nbldLmTE1BXJ0NVlXa1UHu7DDk5xWhqcoOdnbrn1cqVUqa5rlpi1ybNpVD+jTBZ6VZWrZgxI4EpLfn7A1u3dk+MFcrgxj7tXmgejaurK6I1fD49hbW1dRdbdSo7zs/Ph7u7O9PEd3BwYJrdQ4cOhb8WPzVjgRCi1is0RlRsdxCLxcjMzISTkxMjQqHQJD1W3f31VXosk8kM3o/qbKlfXFyMpqYmVFRUdNm1ODk5GX1g8fTp0/j444+RmZmJ2tpa7N27F6mpqQZ7/n5DJoQQ5OXlddv0NvTORCqVIicnByKRCKNHj4ajo6NBjyFfuhSQy8H+80+Az4eczUb16NEgb7yBmJgYjSuQjg7l6pvuQqiMl+5ACOnqTt8TZGayMGWKHVRbT1TSy2YDr7xShevXRaiq6mrZQsHhcCAQ2GPRIke0tUlhZUVgbW0NKytrsNm0lwN8841Ym7+kRtDPgxDCxMyqQhMxqkJbuUx5Ptp3RhS0tBQQEICIiAiDrxBVLV4iIyMZixc+n4/r16/D2toaUqkU4eHh8PX1NeixdYHOsAgEAgwfPtwgcxU9gUgkQmZmJlxdXTF06NBuL+yadn99kR4rFApIJBJlGdpI0mM2mw0bGxu4uLhg6NChEIlEzK7lxo0bWLZsGUJDQ+Hg4IDW1lajlBbb29sRFxeHefPm4eGHHzb48/cbMqGpaoMHD9ZanzZkz4RmPzg6OmL06NHM6rqvAVlqsLeH/K23IHvhBVRdvIhykQhiLy88GBvb7cOUcy7Kiztd9NPrWl+NP5ubWZDJlM9Nd/UKhZKsZDJg61ZvsFjdD2WyWCx4eweBzbaGl5ccbLYYEonwnxo1B4TYoanJpsc7guzsbGZV2puSQ+dymSp07YzoHEV4eHiXgTxjgVq8BAUFobS0FBUVFfDw8EBFRQXKy8uZi6GXl1evB2X1gVwuR15eHsRiMZKTk00+w9LR0YGMjIxeCw20SY8rKyvVXI+9vb27SI8VCgXq6+tRXV2NqKgojeUw+v/7CtUGvJ2dHSPgkEql+OCDD7Bjxw5UVlbCy8sL9957L6ZNm4b58+cbjFimTp2KqVOnGuS5NKHfkAkABAUFdXshN9SuQZmEl4fg4OAuK1BD735kMhnya2vR6u6OqKgo5Ofna72vcmASUK6+Wej8m6I7CJFIaf3el+Yzh3OLqORyAkKUB3N1VV60+PzuH0vh5MSGo6M9AHsoFARisRjNzVI0NUlx+XIexGInZnpcG0G0tLQgJycHfn5+jOtvb0HLZT1BTU0Nrl27ZrbSUlFREbhcLkaMGAFnZ2cQQhjH4/LycjWLFzrzYqhdk0wmY3aDSUlJRiUtTWhvb0dmZqbGQK/eQJP0mEq4b9682aVn1dTUhPz8fGaGRtPAJH3evkiPAe1qLmtra8yYMQMNDQ0QCAT4/vvvcfDgQRw+fBiLFi3q0/thSvQrMtEFmrbYW9CeTGlpKWJiYjReOAxJJnRGgMPhYPTo0cwEtbZzo/Mf9GOhp0H/lz6UGjUC+pVvugeBQiEHi2UFQgBbWxbc3QkABcrLlQfx8yMM8dDAR01gs1mwt7eDQmEHuZz1D1HX4dq1a0xMLO0P0NUvNVs05Y6Agjb6y8vLjeq8qw0KhQIFBQVoaWnBiBEjmCYyi8WCm5sb3NzcMGjQIHR0dDBNfGrxQt9Hd3f3Xl/c6DCktbV1n9WLvYFAIEBmZiYCAgIwaNAgozSeO0u4qfS4tLQUeXnKmGh/f384OTkxaZHGkB4DSjLpbtcnFArh4OCAkJAQLFy4EAsXLuzlqzYP+hWZ6JO22NsLvb6Z8D0NyNIGmnzn7e3N1IBVVzyqr1W1OQiwwWYryUIqvWWKaGtLfauAd96RIi6O/HM76VFfQhWEUHuKrj8IFxflDuTWQKHyeGIx/lF96Ya7uztCQ90wePBgCAQC8Pl8NcmkjY0NGhoaDJbF0ROo7giSkpK6hI0ZG3K5HLm5uZBIJBg+fHi30mN7e3sEBQUxzrfU8bigoAAymUwjSeuCuYch29rakJmZqWYYaWyohmq5ubkhLy8P/v7+kEgkuHTpkk7pMYA+7VrMGYxlCvQrMtGF3u4aqF043SF098M1RM+kpqYGBQUFiIiIwMCBA5kfCv0iqZKJKpGw2Ww4OLDg46O8cCsUXXccrq4Eo0Yp0Nf+rEymgEym+MePrKs9PI2ipw7CqgTi4HBLTaap1Nb5NtWY2LCwMIhEIly9ehX19fUAlE6tLS0t8Pb2Nopba2dQxVhbW5vajsBUkEqlyM7OBpvN7nFpqbOqqa2trQtJ0/6Ak5OTFpFHBzIzM+Hu7o4hQ4aYnEiodVBISAhCQ0NNemxAWebOz89HbGwsMyisr/S4L7sWXfJjC5mYEL0pczU1NSE7Oxs+Pj46VSL0GL3dmRBCUFxcjJs3byI+Pr6L/QQ9Np227UwkLBYLvr7KJrImCSyglMH2hUhcXQnYbOVEPIvFASG3HIOV4VR0xwMMGqRAaysLn38uwcCBt9jG0VE5EOniohya1DTr4uKiPNfOkMvluH79OoRCIeMwQGvaNFuEXgw9PT0NXsOXSqXIzc2FXC43i2Ei3RE4ODhg2LBhfRuMVZnG1mRNQuNrvby8GIsXKjzx9fXtc3+qN6DOw+Hh4SafoQFu+YwNGzZMzXFCm/SYy+WiqKgIDg4OzHeSLni0ZbVo27XomrK/nVMWgX5GJrq+2Kp5Jvr8CCorK1FYWIjIyEgEBwfr9ZjelrlkMhny8/PR1tamNe9ElUzoDkiTWaOSLAxvGyKXy2FllY+PPmLDzy+KkX9WV7Pw3/9aw9ZW2RuhOwtClP6SoaEEERFdz+ebbzTPfQC3Zj9UoU36q5otQhvPZWVluHLlCjOH4e3t3ecdBHVltrOzQ0JCgsl7BNQexcPDwyg7AlVrEhoEVV9fj8LCQkgkEri4uKCtrQ2BgYFmIRI6jKmP87AxQIlEl8+YNqPHzmFquqTHnQcmpVJpt3b6QqEyWtpYEAgEKCkpYf5dVlaGnJwceHh4GITY+xWZ6AKV7+raLioUChQWFqK2thaJiYk9aqxyOBxItGWtakFHRweysrJgbW2NUaNGaV3t0h+vqq2CrnwDQ0EkEiEnJwdWVlZ45pm4f1b8ynJedTULX39N0N7OUsbjqsDRkUDb7JquuQ9V0PeI1ug1fX6dG8+d5zAcHR0ZYtHXloRCIBAgOzvbaBdyXaChUoGBgUZrNqtC1TBx8ODBqK2txbVr12Bra4vKyko0NTUxfZaevpe9AZVeR0VFqUUXmAp1dXUoKCjolWFlZ6NHVenx1atX4ezszLzXLi4uGnctlZWVEIlEsLe3Z3Yt9LdPdy/GLnNlZGTggQceYP69fPlyAMDcuXPxww8/9Pn5bysyUbWI10YmNJdbIpFg9OjRPZ7i7WnPpKdlNDabjYaGBvj6+ppMhtnW1sZcSDWdY2Agwa5dEgg1mDE6OCj/3hdQ6a+vr2+P5J+qUbt0MI3P5zO2JJRYdLn0Njc3M5k0pva5AoDGxkbk5uYiNDQUIZ1jK02AhoYGFBYWYvDgwRgwYAAkEolGuSx9Lw1tcU97FOYQWgBgiDQuLg5eXl59ei5t0mNqqa8qPaaWKdXV1bhx4wYSEhLg6uraZWCSPm9TU5NRJ+DHjRvHeB8aA/2KTHT9yOlFUCaTaVz90zhVFxcXJCYm9upH0ZOeibZGuybQmmpwcDDKyspw/fp1eHp6wsfHx6hDaXRrTpud2s6xr4Sh6/i0Rt7bC7nqYBqVeKq69NKyg7e3t5rAgsfj4cqVK4iIiECQhshjY4Me31wrck1ZJDY2Nmo2H1QuW1xcjI6ODjXH476WFumOoHOPwlSgRBIbG9tnItGE7qTH+fn5sLe3R0dHB4YMGQI3Nzc1xZdqr6WqqgoXLlzAoEHanSf6O1jEmFTVQygUCp0Z6ceOHcPIkSO7uOxyuVzk5eUhNDS0T6vPmzdvgsfjITk5Wet9qBleZWUl4uPjdX5JVZtz9LwEAgF4PB74fD4EAoFBewMUlZWVKC4uxtChQ82yIqTHj46ONpo9iGpELJ/PR2trK1xcXODt7Q25XI6KigrExMSY3J4EUKZSXr9+HTExMWa5kNIskmHDhuld2qGN5/r6ejQ1NTGNZ5ov0pPfVU1NDQoLC412Idf3+HFxcSafIQKAiooKFBcXw9nZGQKBADY2Nl0EEYCScCdPnowRI0Zg69atJvdEMxT61c5EH3TeORBCmEyQYcOG9fmiqWtnQq3JBQKB1ka76rlpS0WkUtnw8HB0dHSAz+eDx+Ph+vXrcHJSTo37+PholXd2B0p2tGfU23Cv3kI1UMrYx+8cESuRSJjAKqFQCBsbGzQ1NcHKyqpPA349geowZEJCglGbqtrQ2ywSmos+cOBAtcZzbm4uAKhNj3e3m6ZEaswslO5gbiKpq6tDaWkpEhIS4OnpqSY9LiwsRH19PTZt2oT77rsPv//+O0aMGIEff/zR5CmahkS/OnN9LpqqF3uqoGptbdWYCdIbdNcz0bfRDqhH6wLdN9rt7e3VegP19fXg8XioqKiAtbU1Qyz6zGDQ96SjowMjRoww+SqHGga2trZ2CZQyBaysrNDS0gK5XI4RI0Yw5HLlyhVGhUNX2sYoLXa2cDd1ap4hs0g6N56p4zFV2rm5uTHlMNXPmRKZORYywK0dmbmIjMvloqCgQI3IOkuPq6qqkJSUhN27d+PGjRtgs9l44403MH36dNx///23pQ19vypzEUJ0KqnOnz+P8PBwODs7M1YQ8fHxBpsXoHX4MWPGqN1OG+2+vr461UCqZS2g9yZxqtPOfD4fCoWC+UJqysKgii1ra2vExsaa3GeJSn8VCgUSEhJMPsNBDQs7OjqQmJio5nxLVTj0vWxvb4ebm5ua31VfQZMZm5ubkZiYaHIiVyWypKQkoyqDqMULn89HY2Mj7O3t4eXlBZlMxrgKmCpUSxXmJhLVgcjuSotNTU2YMWMGgoOD8e233+LUqVM4cOAArl27hgsXLljIpK/Qh0wuXboEd3d3VFZWdgnPMgQaGhpw5coVjB07lrmNBmdFRkbq9I9S1ZYbUvarujLk8XgQiUTw8PBgLobUZ4k6r5pa+kpdBgwxjNcbUBUfi8VCfHy8TiLtfDGkvQFvb2+4urr2+HOjRCYSiZCYmNity4IxoJpFkpSUZFIik8lkTBpiW1ubmiy5JxYvfQUtrZmrtMjn85GXl6dTbNDS0oKZM2fCx8cHe/fuNfl3xVjoV2QCoNvwK0IIzp49C6FQiKFDhxpFndPc3IysrCyMHz++1412TYOIhkZ7ezvTwG/5Jxze09MTkZGRJp+ibW1tZXZthnB+7Slo+bG39vX0Ysjn8xmLF1XZsa46Nt2RAdCLyAwN1SySzjsyU4A6P9AeHSGE2QEKBAJGEOHl5dWrHqA+MDeR0CwVXWKPtrY2pKamwsnJCfv37ze5lY8xcduQCS0h1NTUIDAwENHR0UY5fltbGy5duoRx48YhNzcX7e3tSEpK0ln7V/XpMdUgIqCsTxcXF8Pf35+JCbWzs4OPj0+vV9k9AZX+hoWF6ZRHGwNUDu7j44OoqKg+H181DZHP56vtAL28vLpcqMViMbKysmBnZ4fY2FiT78hUs0gSExNNXlqkhpk8Hk/j70QkEjHqsIaGBkbRpCuWoCeorKxESUkJEhISzNKjoZP9ulST7e3tePjhh8Fms3HgwAGT9xONjX5HJhKJpMtgjVgsRk5ODuRyOezs7ODi4mI0PbZQKMTp06fh5OQEW1tbnStNqtiiogA60Wps0B9xXV0d4uPjmR+R6iqbz+f3aLivp6CrQWNKf7sDHQYMCQlBSEiIUd53VdlxS0uLmtKOzWYjOzsbbm5ueg2sGhqqWSTm2BHR0lpjYyOSkpJ0rrKpxQvdAUokEq3zQfqCNvvNTSRDhgzpNguno6MDjzzyCKRSKQ4ePGhyYYYp0O/JhNpQuLm5YdiwYSgqKgKHw8HgwYONcnwul4vs7GwEBQWZrNHeU6gqthISErT+iOkQFS2H0UyRvg5Kqkp/4+LizFJWoMNwUVFRCAwMNMkxJRIJ02epr6+HQqGAk5MTIiIi4OHhYVIyMXcWCa0UtLS0ICkpqcelNUIIBAIB8362trYyRE0TEXUtDsxNJI2NjcjJydE5kCoSiTBnzhy0trbi8OHDZhEmmAL9mkxqa2tx5coVhIWFMZkHRUVFkMvlGDp0qMGPXVVVhatXr0KhUGDChAk6dyTGaLTrAjUrtLGx6ZFii/54+zooqRrolJCQYJat+s2bN1FSUtKjYTxDQtVCh8PhgM/n9zpXpDcwdxaJao8mKSnJIA1kStS0HEZ31HSmpTNZVlRU4MaNG32WP/cW9DswePDgbhczYrEYTz31FOrq6nDs2DGzLLxMhX5HJlKpFHK5nLFyV80cAJTZF0KhELE6ctR7Aloyqq6uRmxsLDIzMzFu3Ditqy1TNtpVQRvd3t7efVax0UFJPp+PpqYmvQYlVe3b4+PjzaJYojsi6nNkatAekarzrWquCCVq1ZhdQxKuubNIFAqFmmrNGKSpapfD5/MhFouZhY+Xlxd4PJ5ZiYSKdHS5H0ulUjzzzDMoLy/HiRMnzDI8aUr0q6FF4JYypr29XeOEeV/SFjVBJpMhNzcXQqEQo0aNgqOjI2MTrQnmarRTjydDNbp7OihJpb/29vZmsW+nZZWmpiazDEMCt7LiOyt2OueK0KYzn89HaWkp7Ozs1GTHvSUAc2eR0HRIqVRq1Lx41UTEwYMHM30rLpeLwsJCAEBAQADTrzTl+9DS0oLs7GxERER0SyQymQwvvPACSkpKcPLkyTueSIB+SCbZ2dkghGD06NEav6yGzGgXCoXIysqCra0tRo0axRxP0zE6N9pNRSSEEKY2bKxGt7W1NWP8pzooSbMbXF1d0dLSwgxsmvoiRi1sJBIJRowYYRZdfnl5OcrKypCQkKBzGM7Ozg4DBgzAgAEDIJPJNFqSaBs81QbaOzRlzK0qVJv9SUlJJrX9oBYvdAcYEhKC9vZ2Ro6t6nhsTBECTYgMDw/vdixBLpdj4cKFyM/PR3p6ull82cyBflfmam1thbW1tdbVW01NDW7evIlRo0b16TiNjY3Izs5GQEAABg8erHa8kydPqjX1OjfaqWKrpISFtrauz+3sDAwa1Pe3VaFQMLLL+Ph4k2/pCSGoqKhASUkJrKys1PoCvVXf9BRisVit0Wxq76LOPmN9yYpXHTzl8/kQCoVqsmNtfSuaTkh3paYGjRnmcDiIj483+a4UUAY5VVRUICkpiVFCURk33QXScCk6LGnI3WtraysyMzN1fgZyuRz//ve/cfbsWZw8edIsIWDmQr/bmTg4OHS78zBEmYsmMEZFRWlcYaimLWozaiwpYSEuTnvTOje3o0+EQlfjIpHILDnlgHLyv7S0FDExMfDz82PKDdREjw6j+fj4GKXsRHeOrq6uiI6ONkt/gEpfDVFaUw3/ioiIgFAoZMo3RUVFGsO/aKiUudIJpVIpsrKyGMGHOYjkxo0buHnzphqRAMrfqbu7O9zd3REREcH0Aamdvr29vZrjcW+/P3SWKTQ0tFsiUSgUeOWVV5Ceno709PS7ikiAfkgmutCXMhdd6dfU1HSbwEjNHrtrtGvakahC19+7g6pia/jw4WaZHygtLWXMAqkChZYbQkJCIBaLmRX2jRs3DD4oSWvTAQEBiIiIMEt/ID8/H0KhEMOHDzfKVLmDgwMGDhzIOPTSFXZWVhY4HA4cHR3R1NSEoUOHmiULRSKRIDMzE/b29oiNjTU5mQNgvoediUQTVPuAquVF1ahdWl7UVzggEAiQmZmJgQMHdhtsplAo8Prrr+PQoUM4efKkWXaQ5sZdQyZUidTR0aEzgZHD4TCqsttZsdUbqEp/R4wYoXU1bmtrq9YXoIOS2dnZfR6UpKvx8PBws/woaX9AoVCYjMxV+1YKhQLFxcWorKyEtbU1rl27Bh6Px6yyTVFepPJjalFj6u+hqvtxcnJyjy2CrKys4OPjAx8fH7Wo3YqKChQUFMDV1VXN8VjT75sSSVBQEEJDQ7UeS6FQ4O2338aePXuQnp6O8PDwHr/eOwH9jkx0XbQ5HA6Toawv2tvbkZWVBXt7e7VGuyYQQsBmsyEQCBh9++2q2OopVKW/w4cP1/uiZWVlxViVq8o6CwsLmUFJSi66LsxUMTV06NBuJ4qNBYlEwpR1zKFaA5TzTnT37O7uDoFAAD6fj+rqaly7do0pL3Z3IewLVOXHQ4cONfn3kO6Mq6ure0UkndE5aldVbXfjxg3Y2Ngw7yfNvGlvb0dmZiYCAwO7JQdCCN5//33s2LEDJ0+eRGRkZJ/O9XZGvyMTXaA9E30lgQ0NDcjJyUFAQIBO7yZa1vL19WW217QnYGgrks7HpUNY5krlM5T0V1XWGRkZyQxK3rx5E1evXoWbmxtTDlPtA6kGSsXHx5tFSkkvoubq0WjLIqFBamFhYV3Ki7a2tsyFsC99AQqhUIjMzEx4eXkZxOusp1AlEmPZ6Kuq7VRDqwoKCiCTyeDq6orW1lb4+fnpJJJ169Zh27ZtOHHihFEGqW8n9Ds1l1wu73bnIZFIcOLECUycOFHnBe/mzZsoKirCkCFDdDbDVPsjVK2lakVCfYSoFcmVKzYYM0Z7U/zs2Q4kJOh+axUKBQoLC8Hn882i2AJuldZ8fHy6KNsMic6Dko6Ojsz7WVNTAx6Ph8TERLP4FtGShrmcj3uTRaIp70ZVdtzT8hxdjZtrjoUq52pqapCcnGzyWSJCCOrr65Gfn8+Uup2dnZnyoqrFCyEEGzZswKeffopjx44hMTHRpOfaH9HvyERXDrxcLsfRo0cxfvx4rU00eoGura3Vay5A10S7qhUJj8dDe3s72tsD8MQT2nPi9VFzUcWWWCxGfHy8WRRbtD9h6tKa6qAkn88HAPj5+cHf399k8boUzc3NyM7ORnBwsFlmOAyRRaIp/Et1alzXc1IyDQgIwKBBg8zyHhQXFzNkao6h1I6ODmRkZMDHxweRkZFqooiGhgZYWVnh0KFDiImJAZfLxSeffILDhw9jxIgRJj/X/ojbjkwIIThy5Ajuu+8+jT8QOkFPLbl1/Yh6Y40iFArB4/GQldUGPl8EJycnRqJoZ2en15xJR0cHcnJyYGtri9jYWLNkP1dVVaGoqAjR0dHdWmcbC6o9muDgYMZRlq6wfXx8ejTY1xtQtU9ERIRR8nF0wVhZJJp2gXTX0lltRwcig4ODERoaelcTibe3t8adqUKhQGNjI958800cOXIE9fX1GDVqFJ5++mlMnz7dJN+dNWvW4I033sCSJUuwYcMGjfdJT0/HAw880OX2a9euISoqyqjnd9v1TFgsllZFF220Ozg4YNSoUd1ehPoy0e7g4PCP7TmYGjaPV4PGxisAHOHi4oO2Nu0eVy0tLcjJyTF6WUkbtEl/TQkqf7azs2N6NP7+/swKm8fjobS0FPn5+fDw8GD6LIZUMtXW1uLq1atmI1PVLJLk5GSD+lx1tsvprLajxMLhcJCXl4fQ0NBupa/GAi3vcblcJCcnmzzqGFB+F2mfSFuJk81mw9PTE6NHj8a+ffvw3Xffgcfj4ddff8XixYtx6tQp3HvvvUY7x8uXL2PLli16exIWFRWpDdiawhC135GJPhd0TWRCG+2BgYE6a96qg4j0mL1djalKZFVLN+Xl5bC1tWXkiXQ1yOVyUVBQgPDwcAQHB5t8FdjZ48rUqYyAsqSSnZ0NDw+PLmaFqsqbiIgIZlCytrZWbVCyr0om6jxsrmZ/Z3sSY8qPra2t4efnBz8/vy5qO7FYDEdHR1hZWUEsFpvUqoYarPL5fLMTiYeHR7eCA0IIfvnlF6xYsQL79+/H+PHjAQCvvfYaGhsbjdrnEwgEePLJJ7F161a8//77ej2G+uqZEv2OTPRBZ3lwbxrtxsgg6exxRVeDNJvczs4OAoHA7GUlmUxmNo8r2p8ICgpCeHi4TjLoPChJyZoOSlK1nb6DknRXVlVVhaSkJLMIHsyZRULVdoQQVFVVISwsDGw2m3E1oA1nb29vo0XsAl2JxBz9QrFYjMzMTLi5uXXrOUcIwa5du7B8+XLs3r2bIRIKXT3ZvmLRokWYPn06HnzwQb3JJCEhASKRCEOHDsVbb72lsfRlaNyWZELlwaqN9uTkZJ3lGlNax3M4HGZXQsOsGhsbYWVlhWvXroHP55ukJ0ChKv2Nj483S4+GztH0tj9ha2uLwMBABAYGMmTN4/EYsqZ9Fm0ybtrorq+vN8j8Qm9g7iwS4FafSHWWJzQ0VC38q6ysTOP8hSFACEFhYSHzOZiDSOh0v6urq85Zmt9//x2LFi3Czp07MWXKFBOeJbBz505kZWXh8uXLet3f398fW7ZsQVJSEsRiMX7++WdMmDAB6enpuP/++416rv2uAQ9oz4GnuHTpEvz9/cHlco3aaDcEpFIp43ibkJAAW1tbpifA5/PR0dEBDw8P+Pr6Gi1USTUn3Rw9GuBWxK8x5mg05V/QngB9TxUKBfLz85lGtzkuYObOIgGUSaJXrlzpYqPfGarzFzT8S/U97UtKZ2FhIaNcMyeR0On+7q4Ff/75J+bNm4eff/4ZDz30kAnPEsz0/5EjRxAXFwcAGDduHOLj47U24DVh5syZYLFY2L9/v5HOVInbkkwuXrwIoVAIV1dXnU6ytNFujgwSuhuws7PTqtii0808Hg9tbW3MUJ+Pj49BlD1U+ksbrOZQ6lCjvvj4eKM3+6mMm76nNKhKIpGAzWYjKSnJqCmI2mDuLBLgluAgNja2Rw1ZTeFfbm5uanYk+j5PTzLjjQGpVIqMjAw4OjrqtIk5fPgwnnrqKXz33Xd47LHHTHiWSuzbtw+zZ89W22XL5XKwWCyw2WyIxWK9SqQffPABtm/fjmvXrhnzdPsnmXTOgVdFfX09s7obPny4yRrtPQVVbNGLhz6rUJFIxMyyNDc3w9nZmVEx9aYkU11djcLCQrNZk9AyZH19PRITE81SVmptbWX6RHK5nHHm9fHx0Stn3FDnYM4sEkD5XSgqKkJsbCy8vLz69FwdHR3MjqWxsREODg5q7ryaXh8hhBF+JCcnG8U4UxekUiljXKmrxHjixAnMmTMHmzdvxlNPPWWWz6ytrQ0VFRVqt82bNw9RUVFYuXIlYmJi9HqeRx55BI2NjThx4oQxTpPBbdMzoSFR169fh5ubGzw8PPSyRjFGo10XqGJr0KBBCAoK0vuLaGdnx8g5JRIJs7q+ceMGY6ft4+PD2JNrg6r0V5+hTWOAyl6phb45Lh4dHR3Iz8+Hi4sLhg0bBrlczlwEMzIymERJQ/cEVGHuLBJAWS4pLi5GfHy8Qb4L9vb2CAoKQlBQkJrJp7bwL0okzc3NZiWSrKws2NnZ6SSS06dP4/HHH8fnn39uNiIBlDY6nQnD0dERnp6ezO2rVq1CdXU1fvrpJwDAhg0bEBISgujoaEgkEmzfvh1paWlIS0sz+vneFmRCcyWoFr22trZb52Bz9Ueov1RZWRmGDRvWJ223jY0N02ymP1jloGSWWnO/sx9Tf5D+SiQSpimenJxscgt94FZZibov09KAqjMvtSIpKCiAXC5X6wkYQqBg7iwSAIznW2JiolGkoqomn4QQpndVUlLCzAhJpVJIJBKjWfnrgkwmYyIddFnpX7hwAY8++ig++ugjPPfcc2YjEn1RW1uLmzdvMv+WSCR49dVXUV1dDXt7e0RHR+PAgQOYNm2a0c+lX5a5pFIpU5qiFyapVMo0TouKiiCTyRAdHd3lsYQQRjZsyrIWJbyGhgbEx8f3KZFP13EaGxuZBj4hRC1QqaCgAFKplGn2mxodHR1q1uXmcN2lWSgDBgzQS36sakXC4/GYBMS+DErSRre5ZODArVCphIQEs0igBQIBrly5gvb2digUCjXZsalKjDKZDFlZWbCystIpw758+TJSUlLw3nvv4eWXX+73RNLf0K/JhK4unZyc1BrYpaWlaG9v7zINSvsj5lJsSaVSxMfHm2z1RWNgeTweuFwuRCIRbGxsEB4eDl9fX5PvCKhqzFxmiYByeDU3NxeDBg1CcHBwr56DDkry+Xy0tLSo9a70GZSk/Ym+7k57i86zNOYwzqS5OG1tbUhKSmJSI1V9rlRLjMZYdMjlcmRlZYHNZuuMG87OzsaMGTPw5ptv4pVXXrEQSS/Qb8mEy+UiNzcXwcHBXZL2ysvL0dTUhISEBADmbbSrzm8MGzbMLPMb9CLu5uYGR0dHxujPWDYkmtDY2Ijc3Nx/bGZMrxoDgLq6OhQUFBhUcEB7V/QiqGtQ8ubNmygtLUVcXJxZelXU56q2ttZoFu660JlIOn/3FAoF48PG5/PVMm8MJY+Xy+XIzs4GAJ2RCvn5+Zg2bRpeeeUVrFq1ykIkvUS/JJOSkhIUFhYiOjpaY1xpZWUl6urqMHz4cLM22pubm5GTkwM/Pz+zrcS1SX+pGSVdXbu4uDB9FkPbVtCL+JAhQ8wSLwvcajIbQq2kDaquBtTpmBKLu7s7KioqGNGDOcpKqlPlvXUf7iuocWV7e7teMmxVKTefz0dbWxtcXV2ZXYuDg0OPf1dyuZxJykxMTOyWSK5evYqpU6fipZdewurVqy1E0gf0SzIpKyuDg4OD1oZhTU0Nbt68iZEjR0KhUEAul5u0rAUoL6BXr17tUzmlr9BX+nvLjJKHxsZGJkfEx0e7GaW+oB5X5izpqPYGTOVHpFAomBIjn8+HSCQCAISFhWHAgAEmn2VRld6aa4ajp0SiCaopiI2NjT0O/5LL5YwTdUJCQreVguvXr2Pq1Kl49tln8eGHH1qIpI/ol2RCZwK0gcfjobi4mCGT21Wx1ZdzoNLfnpZTVM0oGxoaGMsMqgzT932k5ZSamhqzr8RpqJY5Sjr0Il5fXw8/Pz80Nzczw6eqq2tjgpaVWltbkZSUZBbFFHUYEAqFBhsM7bwTJIR0G/6lUCiQm5vLiHW6I5LS0lJMnToVjz76KD755BOzuBHcaeiXZKIrbbG+vh45OTmIiIiAj4+PyVaBqoqthIQEszU26Qo0ISGhTxdQaplBV9csFkstpljbD4xevFpaWpCQkGCW/AnVczD3SrxzFolIJGIugHQnaKxBSdWLeGJiolkUfPQcOjo6kJiYaJTfIxWb0PdVKBQy4V+0J0jt/BMTE7sVn5SXl2Pq1KmYOXMmNm7caCESA+G2IhNqjSKTyVBRUcHYZdBGszGJRdVx15SKrc7nQFVjhpb+Un8rSizUi6mzGaVMJmNWf+aSH9NShkQiMdrFS59zUL14aTsHmiXC4/FQX18PKysrRhTR10FJfc/BmFAoFMxwqrGt9FUhFAoZYmlubgaHwwGbzUZMTEy3A83V1dWYNGkSJk2ahM2bN1uIxIC4bchEW6O9o6MDXC4XPB4Pra2tBve2ApRf3JycHLMqtmiYlCmSGTvPXVAzSg8PD1RXV8PW1lanJ5qxIJVKmYAnc7kfq2aRxMfH630BVR2U5PP5fRqUpE1m2hswx2AoLSuJxWKTEknnc8jLy0NraytcXV3R2NgINpvN7FhUHaRra2sxZcoUjBkzBt9++61ZZqDuZPRLMukc3Uulv7oa7Z29raiCydfXt9dlEKrY8vf3N5tBX1tbG7Kzs+Hl5YWoqCiTr6ba29tRVVWFyspKEELg5uYGX19feHt7m7S8pGrfbq6BSENlkWgblKQXwe4WQnSiG4DOJrOxQImE7g7NRSSdG/6dHaQrKirw008/Ydy4cdi9ezcSExPx448/muU9u9PR78mkt9YoEomEIZbGxkY4OTkxxKJvjZ8qtsyVDw4oh/Dy8vLMOr9BJ8oDAgIQFBSkli1O31eqDDMWaCSzpnRGU8GYWSS0bMPj8bodlKQ7Mw6Ho3MQz1igZUaZTGa2XREhhBEdaIs8JoSgrKwMmzZtwsGDB1FRUYERI0YgJSUFM2fORHR0tEl+T/pktwPAqVOnsHz5chQUFCAgIAArVqzAggULjH5+hkK/JhNDTbRLpVLmh9rQ0AB7e3uGWDRJY+mXsLy83KhzC7pgbtdf4NYcS3h4eBejQhqmRN9XOzs7hlh0mVH2BNR1NzAwEIMGDTILoZoyi6TzoCSNf3Z3d0dJSQkTaXA3E4mqcWR3fbvGxkbMmDEDISEh+OKLL3D06FHs378fGRkZKC8vN/oO5fLly3j00Ufh4uKCBx54QCuZlJWVISYmBi+++CLmz5+Pc+fO4aWXXsKvv/6Khx9+2KjnaCj0SzKRy+WQSCSMPNiQE+0ymYy5ANbX18PGxga+vr7MBZB+URsbG82m2FKdnTDXJDWgnOe5du2aXv5S1JGXvq/UjLKvjebGxkbGdTckJKRXz9FXmDOLhMpj6+rqwOVywWKx4OfnxwgjTEkoqn0aXdJbY4FmotB5mu7KgS0tLZg5cyZ8fX2xZ88eNdKRy+VGf++oym/Tpk14//33uw21WrlyJfbv36+WObJgwQLk5ubiwoULRj1PQ6FfksnHH38MPp+P1NRUDBs2zGgfumr0K5/PZ45DQ5TMJTelAUJ9lf72FnSWhu7MPD09e/R4apfB5XLV5gN6egGkZonmnKzvD1kkIpEImZmZcHZ2RmBgIDPUJxaL4enpCR8fH6OldFKoTpWbq0+jmtKoy8q+ra0NKSkpcHFxwf79+82ivpw7dy48PDywfv16nQmJ999/PxISEvD5558zt+3duxePPvoohEKhWXaAPUW/7EINHToU33//PSZNmgRfX1+kpKQgNTUViYmJBi0vqFq509Uni8WCTCbD33//zfzNWFkXnaEquzWXXTcdBKR2/73ZmbHZbHh6esLT01PNjPL69etMpC69AGr7kdCY356mAhoS/SGLRLW8RrPKPT09ERkZifb2dvB4PFRWVuLq1atwdXVldoOGHJTsL0RSVFTE5MZ399tob2/HI488Ant7e+zdu9csv6OeZrfX1dV1iVH29fVlKinmKnP3BP2STKZPn47p06ejvb0dBw8eRFpaGmbOnAk3NzfMmjULqampGDFihMF2LE1NTcjNzUVAQAAiIiKYXAYej4crV64wNu90ZW0MYlGV/iYnJ5vlByuXy5khvBEjRhhkZ8ZiseDm5gY3NzdERERAIBCAx+OhoqICBQUFjILJx8cHtra2aruihIQEo8f8akN/yCIRCoXIzMxkVHyquyIWiwUnJyc4OTkhLCxMbVCyuLiYGZSk0QS93VFRw0RCiFmJ5Pr16+Dz+UhOTu72e9nR0YFHH30UALB//36zDNRWVlZiyZIlOHLkSI+ITFPvVtPt/RX9ssylCR0dHThy5AjS0tLw559/wt7eHjNnzkRqairuueeeXn/JaS52ZGSkRsUWXVnTWRapVMpc/Ly8vAxCaOaW/gJKkYLq7IQpBuA6OjoYxR01o2SxWIzU0xz9KqB/ZJG0t7cjIyMDfn5+Pe7TqA5KNjQ0gMPhqBlS6vv9ohJkFoul03nXWKC2PXV1dUhOTu52xyUSiTBnzhy0trbi8OHDZrH4AXqX3X4nlLluGzJRhUQiwbFjx5CWlob9+/eDzWZjxowZmD17Nu677z693njVJvewYcP0UmwRQtDW1sYQi0gkYko23t7evSI0Kv0dOHAgQkNDzVaTz87ONqtKiEbstrW1gRBiUDPKnsDcWSSAcnGRmZmpd7hXd6D9K9oXlMvlTJ9Fk78VBSUSfbJAjAVCCEpKSlBbW6uTSMRiMZ566ilwuVwcPXrUbDtaoHfZ7StXrsQff/yBq1evMrctXLgQOTk5lga8qSCVSnHq1Cns3r0b+/btg1QqxYwZM5CSkoIHHnhAo2xQ1d8qPj6+VytgQgja29sZYmlvb2d+pN7e3nqt7KlaypzSX4FAgOzsbLPOb6jmxdO+GG0y19fXw9raWi2m2FjEYu4sEuBWwz84OBhhYWEGfW7VQUnVzJvOg5L9gUgAZRRFdXU1kpOTuy1XSaVSPPPMMygvL8eJEyd6LBgxBTo34Dtnt1Np8Pz58/Hiiy/iwoULWLBggUUabC7I5XKcPXuWIZa2tjZMmzYNqampmDBhAuzt7VFXV4cDBw4gJiYG8fHxBvOWos1QHo+HtrY2uLu7MxfAzsfoL9Lf5uZmZGdnIygoqM8r4N6CltcAaLQm6a0ZZU9APw9zZpEAtz4PUzX8NQ1Kenp6gs/nw8bGxqxEopoU2Z2iUSaT4bnnnkNhYSFOnDgBHx8fE56l/uhMJs8++yzKy8uRnp7O3OfUqVNYtmwZM7S4cuVKy9Bif4BCocDFixexe/du7N27F/X19Rg3bhwyMzMRGxuLXbt2Ge2H0rkX4OrqytiP2Nraml36C4ARF5hzul8sFiMrK0vv8lpnM0qpVKqmDOtNmZE2d+vq6syWTAjcmqcx1+chkUhQV1eH0tJSyGQy2Nvb9yqawBAoKytDRUUFkpOTu/085HI55s+fj+zsbJw8edJs/S0LlLhjyUQVCoUCW7duxdKlSzFgwADU1NRg4sSJSElJwdSpU+Hi4mK0Y4vFYoZYmpqawGazweFwEBsba7a6LpXdxsTEmG0lJxQKmajhoUOH9niHQftX9L2lZpQ9KTPSAbiGhgazJRMCt3LrBw8ejMDAQLOcg1QqRVZWFmxsbBAdHa3mbwWAKYUZe1CSKvl0CTDkcjlefvllnDt3Dunp6WZ73yy4hbuCTH799Ve88MIL2LBhA55//nnk5eUxO5bS0lJMmDABKSkpmD59utFWYdTXCQBsbW3R1NTENJmpX5ixV3+qoVrx8fFmIzOaWe/v74+IiAiDvO7OZUbqHq3NjFJbFompwefzkZeXZ9a+mSqRxMXFqRE7lcnTchgdlKTkYkjVX0VFBW7cuIGkpKRuF3gKhQLLli3D8ePHcfLkSbPNAFmgjruCTI4dOwZCCCZOnKh2O12Z7t69G3v27MHVq1cxbtw4pKamYsaMGfD09DTIhY5Kfz09PZkmt2riYX19Pezs7BhbF0MHKAH9Y7IeUJZzcnNzjWpcSWcu6G5Q1YzS0dGRsS03Zw4IcEuCHBMT02VgzVSQSqXIzMxkYgW62yFS0Ql9b1Xz2n18fPq0s6PiB32IhCqf0tPTDS5SsKD3uCvIRB9QGSIllpycHIwZMwYpKSmYNWsWfH19e3Xh00f6S32tuFwu4xdGL36urq59vuCqqqUSEhLMtgqnfRpTlnM6m3za2tpCoVDA2traYPGyvQGdbzLnhD8lEtqz6mmpsXOipIODA7Mb7MmgZGVlJUpKSpCYmNit+EGhUOCtt97Crl27cPLkSURGRvbofC0wLixkogF0CjstLQ179uzB5cuXMWrUKMyaNQspKSkIDAzU64dCpb898Zai6iXqa6Vq+eLu7t5jYpFIJMjJyQGLxepRkJOhQd+LYcOGma1PQ61JaKRBb4f5+go6yxIXF2c2GatEIkFWVhYT+NbX105tP6icm763NKBK2/NXVVWhuLgYCQkJcHNz0/r8hBC89957+PHHH3Hy5EkMGTKkT+drgeFhIRMdIISgqqoKe/bswZ49e3D+/HkkJSUhJSUFKSkpGDhwoFYL+4qKil4ZJVJoMkykxKKPLLajowNZWVlwcnIyW5gUoGyqlpWVmVUG3TmLBAAzzMfj8aBQKNQsc4z1XlVWVqK4uBjx8fFmey8kEgkyMzPh4OBg8FwW4Nb3lu4I6aAkTZSkCxpKqrpscwghWLduHTZv3owTJ04wn58F/QsWMukBCCGoq6vD3r17kZaWhtOnTyM2NpYhlkGDBkEikWDDhg0YNWoUEhMTDWYJQghRu/jJ5fJuL360ye3r64vBgwebZYaEWmHU1NQgMTHRqKq57qAri0TVjJI2mWmcrre3t8F2c5RUda3CjQlKJDSt0ti7MVXVHR2UdHd3h62tLbhcLhISErolVUII1q9fj88++wzHjx9HQkKCUc/Xgt7DQia9BCEE9fX1+P3335GWloYTJ04wwU1CoRDHjh0zmu6dTjLT6XuJRKI2b9Ha2mr0JrcuqDb8ExMTzWK4B/Q8i4Q2mSmxCAQCZgBVV5xud6BDquYkVVMTiSYIhUKUlpairq4OAODi4sKQdmfbHEIIvvzyS6xduxaHDx/GiBEjTH6+FugPC5kYADRQa+rUqZDL5RAKhfDz82Os8435wyWEQCAQMMQiFApBCEFgYCAiIiLM0iORy+XIz8+HUCg0q+zWEFkkmswoVeN0dYHKsaurqw26U+0pxGIxk4kSHR1tFiIBbkVhx8XFwdnZWa3PYmtrCzc3N9y8eRMTJkzADz/8gNWrV+PgwYO45557zHK+FugPC5kYANevX8eECRMwceJEfPPNN+jo6MCff/6JtLQ0HDp0CP7+/pg1axZmz56NhIQEo/2QKyoqUFJSAl9fXwgEAggEAmaQz8fHxyTKJalUitzcXCb7wlwNf2NkkUgkEqZc09DQwNi8a5Nz0+l6LpeLpKQks+3O+guRUCl0XFxcF2NVKjy5dOkS/vWvfzFx3atWrcKyZcuMPlC6efNmbN68GeXl5QCA6OhovPPOO5g6darG+6enp+OBBx7ocvu1a9cQFRVl8POj0eX9GRYyMQCam5vxyy+/4KWXXupyQREIBEwmy19//QUPDw/MnDkTs2fPxvDhww3S6FXtTah6SwmFQmZV3draCjc3N8bWxRi7BbFYjOzsbGb4zVwNf1NkkXSOf+5sRgkAhYWFqK+vN+t0vVgsRkZGBlxdXREdHW22bAwej4f8/HydUmhCCH7++Wd8+umnSExMRHZ2NiorKzFp0iT8+OOPRus1/fHHH+BwOBg0aBAA4Mcff8THH3+M7OxsREdHd7k/JZOioiK1sqW3t7fBvveUQOj/trW1oaCgADY2NkhMTDTIMQwJC5mYEEKhUC2TxdHRkclkGT16dK+8pRQKBQoKCtDS0oKEhAStq1+RSAQejwcul8uUa+iQpCFCsGiTm160zL36NWUWSWczSgCwtraGTCbT6XhrTNC4X3MTCZ3y1yULJ4Rg165dWLx4MXbv3o0pU6YwUb2HDx/GkiVLTPoaPDw88PHHH+P555/v8jdKJk1NTUYhuOzsbPzxxx9YuHAhvL290djYiOHDh8PKygrFxcWYP38+li1b1q9mbSxkYiaIRCIcP34ce/bswe+//w4Oh8PsWMaMGaNXeUg15jchIUFvB2SxWAw+nw8ul8tMiFNi6c2Frz8ox4D+kUVCP5PW1lZwOBzIZLI+m1H2BiKRCBkZGWpxv+ZAfX09cnNz9Zry37t3L/71r3/ht99+w4wZM0x0hl0hl8uxa9cuzJ07F9nZ2Rg6dGiX+1AyCQkJgUgkwtChQ/HWW29pLH31Br/88guefvpprFy5EkuXLsXy5cthbW2NVatWobS0FM888wweeOABvPPOO/1GKm0hk34AqVSK9PR0pKWlYd++fZDJZEwmy7hx4zSSBC0pWVtbIy4urtcXKTohzuVymT5AT0KpqG26OcO9gP6RRaJQKBjhQVJSEqytrdXMKIVCYY8zb3oDSiQ0o8Zcnwk1sBw6dKjOXeKff/6JefPmYfv27Zg9e7aJzlAd+fn5GD16NEQiEZycnLBjxw5MmzZN432Liopw+vRpJCUlQSwW4+eff8bXX3+N9PR03H///QY5nx07duCpp57Cm2++ifr6eixatIgJ1srIyMDMmTMxatQo/Oc//0FsbKxBjtkXWMikn0Emk6llsggEAkyfPh0pKSlMJkt+fj4uXryIUaNGGbSkRPsA1NbFzs6OIRZN9hh8Ph/5+flmtbHvL1kk1LKmO78vKjnm8/lobW2Fq6sr8/4aotQI3Co3mptIqKX+kCFDdBpYHjp0CE8//TS+++47PPbYYyY6w66QSCS4efMmmpubkZaWhm+//RanTp3SuDPRhJkzZ4LFYmH//v09PjYhhPmsRCIR09Pcu3cvE4515MgRPPjgg0wPJT8/HzNnzkRkZCTWrFmDpKSkHh/XkLCQST+GXC5nMln27duHhoYG3HfffTh79izmzJmDzz77zGgXC7lcjoaGBoZYrKys1BrMVOJpzpz0/pJFIpfLkZOTA7lcrreCTZcZZW9lzJmZmfD09ERUVJTZiSQqKkqnjdCJEycwZ84cfP3113jyySfNds6a8OCDDyI8PBzffPONXvf/4IMPsH37dly7dq3Xx+RyufD19UVHRwd+++03PPvsszh27BgmTZqEp556CmvXrkVAQABDPgUFBbj//vuxe/dug5XYegsLmdwmUCgU2LhxI1asWIFBgwahoqICEydORGpqKqZOnWrU+QWFQoGGhgZmVU1lm+Hh4Rg4cKBZmu39JYuERtxS77PelBs7m1HSHaG3t7feRp8dHR3IyMiAl5eXWYmkqakJ2dnZepl5nj59Gv/3f/+Hzz//HPPmzetXRAIAEyZMQFBQEH744Qe97v/II4+gsbERJ06c6NXxPv/8c3z33XdIS0vD5MmTkZiYiJ07d4LD4eDgwYOYOXMmnnvuOaxevVqNUNra2sw2v6QK03QDLegztm/fjjfffBM///wz/u///g+5ubnYvXs3PvroIyxcuFAtk8UQTsOqYLPZjK9SSUkJKisr4e3tjZs3b6KiokLN1sUUxKKaRTJ8+HCzDUVKpVJkZ2fDysqqT1Joa2trBAQEICAggNkR8ng8ZGdn62VGSYnE29vbrAII2j+LjIzUSSTnz5/Ho48+io8//rhfEMkbb7yBqVOnIigoCG1tbdi5cyfS09Nx6NAhAF0z2zds2ICQkBBER0dDIpFg+/btSEtLQ1paWo+Oq1rSCgkJgZ+fH4YPH47o6Gjs2rULgHLBMnXqVBw6dAhTp06FQqHA6tWrGdk73ZGrlsrMAcvO5DbBvn374OLigvHjx6vdTqfvqXX+tWvX8MADDyA1NRXTp083WCYL3QnQuQlHR0cmOIk2mKlyydfX12hmifr0JkwB6rpra2urV+Rwb0ANE7szoxQKhcjMzISPj49edjHGQktLC7KysjBo0CCd/bPLly9j1qxZ+O9//4uXX37Z7EQCAM8//zyOHz+O2tpauLq6IjY2FitXrmQykDpntn/00UfYsmULqqurYW9vj+joaKxatUprw14TRCIRRo8ejcceewyvv/46AGDixIk4c+YM4uPj8dNPPyEyMhIymQwsFgscDgdnzpzB2LFjMXPmTPzyyy9mK+1qgoVM7iDQ4UVKLLm5ubjvvvuYTBYfH59e/XCpSqm9vV2rPQr1C6OzLNQs0dfX12CSWJlMhpycHBBCzGqnTyfKqQOxKXZj1IySlsNEIhHc3NzQ2toKPz8/s5a2WltbkZmZifDwcAQHB3d73+zsbMyYMQNvvvkmXnnllX5BJOZCfX091q5di++//x6vv/46XnvtNZw8eRItLS3Ytm0buFwuvvnmGyQkJEAikTALp0uXLuHSpUv497//beZXoA4LmdyhoDb4NJMlIyMDo0ePZjJZAgIC9Poh07kJmUyGhIQEvbPVBQIBQyw0n51O3/eGBCQSiZoU2lzT9aqDgL3JrjcEqMlofn4+OBwOpFKpQcwoewNKJPrY1uTn52PatGl45ZVXsGrVqruaSCgaGxuxadMmfPbZZ3j33XexZMkSAMBff/2FzZs3o7a2Ft988w2SkpJQVFSE3bt3Y9WqVcz3ztylLVVYyOQuACEElZWVTCbLhQsXmEyW1NRUBAcHa/xC0gs47Qn0dndBJbFcLpdx4aXEos+gZecsEnNN1/cX2W17ezsyMzPh7++PQYMGMe4GqmaUtBxmzOn7trY2ZGZmMu7U3YEaoS5atAjvvvtuv7kAmgtSqZRZVO3Zswc7duzAnj17sG7dOrz22msAgMOHD2PLli3IysrC/PnzsWbNGrzwwgv49NNPzXnqWmEhk7sMhBDU1tZi79692LNnD5PJkpqaipSUFISHh4PFYqG4uBhXr15FaGioQV2PqQsvl8tlZi3o9L2mFbWuLBJTob29HVlZWWZvcre3tyMjIwMBAQFM5IEqJBKJmjKMRulqM6PsLQQCATIyMphh1e5QVFSEqVOnYt68efjwww/veiJRxUMPPYTm5mYEBQUhOzsbxcXFeOWVV/D+++8DUAoVdu7cib///hvTp0/H22+/DaB/7UgoLGRyF4OWSyixnDhxAlFRUXjggQfwyy+/YM6cOVi3bp3RvrR01oLL5aK5uZmxd/fx8YGDg0OPs0iMBYFAwOwEIiIizH4egYGBDOl3B21mlN7e3r2KgO58HgMGDEB4eHi39y0pKcHUqVMxZ84cfPzxx/3e+dbYUCWBb775Bu+99x7OnTuHkJAQVFdX48cff8S6deuwaNEifPjhh8zjBAIB02yXy+VmK/N2h9uKTNasWYM33ngDS5YswYYNGzTex9TW0HcKaJLj+vXrsW7dOgwcOBBWVlZITU1Famqq0c0bqb07j8dDY2Mj7O3tIRKJEBAQYNbmMi3l0Avn7UIknUFnheiuBQBTCvPw8ND74kR3RoGBgYzDrjaUl5djypQpmDVrFjZu3HhXE8n8+fO7DD++/fbbOHbsGC5cuMDcxuVy8f777+Orr77CO++8g9WrV6s9pj/uSChum0/38uXL2LJli94eNEVFRaitrWX+i4iIMPIZ3t5gsVi4fPkyNmzYgE8//RQZGRl46623UFRUhPHjxyMhIQFvv/02srKyoFAoDH58GxsbDBgwAImJiYiLi4NIJIK9vT1qampw4cIFlJSUoK2tDaZc+7S0tDClHE0lJVOBlpQGDBjQ6/Ogs0JDhw7F2LFjmR5YYWEhTp06hby8PNTV1UEmk2l9DipDDggI0LkjqaqqwvTp0zF16tS7nkhyc3MZAldFTEwM6urqkJ2dzdzm6+uLqVOnwtHREe+99x5+++03tcf0VyIBbpOdiUAgQGJiIjZt2oT3338f8fHxOncmxrKGvpPxwQcfIDQ0FE888YTa7QKBAH/99RfS0tJw8OBBeHp6qmWyGPJC0TmLpHOpxsbGBj4+PvD19dXoF2Yo0AE8Q4Zr9QZ0ZxQUFKTzAt4baMpo1xSoJhQKkZGRAT8/P52lvtraWkyePBn3338/tm7d2i9LMqaETCZjxCubNm3C888/D1tbW2RnZ+PFF1/E6NGjsWDBAiY35fTp09i6dSsWL16MkSNHmvPUe4Tbgkzmzp0LDw8PrF+/HuPGjdOLTIxlDX23QygU4vDhw0hLS8OBAwfg6OiIWbNmMZksfblw6MoiUZ0O5/P54HA4zEWvLz2AzqDeUuY0sARuEUlwcDDCwsJMcsz29namFEYFEu7u7qiuroafn5/O3hWXy8XUqVORnJyMH3/88a4nkl27duHAgQP44YcfUFdXh8GDB2Pw4ME4ffo07Ozs8NNPP+HDDz9ETEwM7r33XkRERGDFihWYNGkSc43rrz2Szuj3ZLJz50588MEHuHz5Muzs7HSSiSmsoS1QQiQS4dixY0wmi7W1NbNjuffee3s0T9LTLBKFQsEEUvF4PLBYLHh7e8PX11er7Yg+oDsjfUwKjQlKJPqopYwFkUiEmpoalJWVQaFQdMm96Uwq9fX1mDZtGoYOHYodO3aYLLulP+Onn37C888/j19++QWPPvoocnJy8OSTT8LGxgZnz56Fo6Mj9u7di/3792P//v0ICgpCVFQUdu7cCaB/90g6o1+TSWVlJZKTk3HkyBHExcUBgE4y0YS+WENboB+kUilOnjzJZLLI5XLMmDEDqampGDduXLfDjn3NIlEoFGq2LnK5nCGWnjSXabSsOZ2QAeUgYFZWllmJBLiVi+Lp6Ynw8HDU19eDz+ejvr4etra28PHxgVQqRVRUFFpaWjB9+nSEhYXht99+M7rNTU8z2wHg1KlTWL58OQoKChAQEIAVK1ZgwYIFRj3PmpoaLFmyBCKRCJs2bUJQUBDy8vLw5JNPAgDOnj0LV1dXiMViCAQCiEQixtfsdtmRUPRrMtm3bx9mz56t9obK5XKwWCyw2WyIxWK93mxDWENboD9oJsuuXbuwb98+CIVCTJ8+HbNmzcKDDz7IzJMoFArk5eWhubnZYFkk1HaEzrJIpVI1Wxdt3xdaYtMVLWts0Iny0NBQnYOAxgTNjqfzPaqrY9Vy41NPPYWGhgZ4eXnB1dUVx48fN4mDbU8z28vKyhATE4MXX3wR8+fPx7lz5/DSSy/h119/ZfJC+gqaM9LS0qL2Xf7jjz8YafRLL70EuVyOa9eu4emnn4ZEIsGZM2e6LKLoc91O6Ndk0tbWhoqKCrXb5s2bh6ioKKxcuZJJHdOFvlpDW9B7yOVyXLhwgclkaWpqwuTJk5GSkoK//voLV65cwdGjR41iWKfaXOZyuRCJRGoRurQMV1NTg8LCQrPG/QK3zBLN3fSn3mPUMqa7Mgufz8fcuXNRU1MDgUAAoVCImTNn4qWXXsLo0aNNeNbdZ7avXLkS+/fvV1tQLliwALm5uWrS3L7iyJEjePnll7Fq1So888wzDCGsWrUKX375Jc6dO4fY2Fgm2/7ZZ59FUVERKioqzBbsZij0a+pzdnZGTEyM2n+Ojo7w9PRkiIR+aBQbNmzAvn37UFxcjIKCAqxatQppaWlYvHixxmOsXr0aLBZL7T9dJY5Tp04hKSkJdnZ2CAsLw9dff224F32HgcPhYMyYMdiwYQNu3LiBI0eOICgoCIsWLcKff/6JsLAwHDx4EG1tbQY/NovFgouLCwYNGoR77rkHI0eOhJOTE8rLy3Hq1ClkZ2fjypUruHbtGuLi4ixEAuW8T2ZmJlxcXHQSiUAgYAKtcnJyUFVVhcOHDyMwMBD19fUmO2e5XI6dO3eivb1dK4FduHABkyZNUrtt8uTJyMjIgFQqNdi5XLp0CcXFxViwYAGWL1+OX375BXK5HKtWrUJSUhI++OADpsc3ZMgQfP/993j22WdveyIB7oA8k9raWty8eZP5t0QiwauvvqpmDX3gwIFuraGjo6Nx7Ngx5t/dlc7Kysowbdo0vPjii9i+fTuzXfb29jbYdvlOBZvNRkJCAtavXw9/f3989tlnOHPmDNauXYsFCxbgwQcfREpKCqZNm2bwTBYWiwUnJyc4OTkhPDwcQqGQmUVisVgoLy+HUCiEj4+PXn5hhgSVIevjumtMUCJxdnZGdHR0t++/UCjEo48+ChaLhT/++IMJJxs5cqTJ5KydM9v37t2rNWK3rq4Ovr6+arf5+voy0nNd0cLdgRZ3WCwWFi5ciKamJshkMshkMuzbtw87d+7ETz/9hEceeQSbNm3CpUuXMHPmTADA0KFDbzvVljb06zKXKbB69Wrs27cPOTk5et3fVNvlOxW//vorPvnkExw6dIjZCRBCUFBQwFjnFxUVqWWyeHh4GFzRUl5ejrKyMiYTRdUo0RjZ7NpAiUSfHBBjQiqVIjMzEw4ODjq92EQiER577DEIBAIcOnTIbKvqnmS2R0ZGYt68eVi1ahVz27lz5zBmzBjU1tb2SnChqa9BCMGGDRuQk5PD+Gi98sorKC0txauvvoq33noLgwYNwvHjx29r4tCEfl3mMhWKi4sREBCA0NBQzJkzBzdu3NB6X1Ntl+9UzJkzB2fPnlUrKbFYLMTExGD16tXIzc1FXl4eM/AWFhaGWbNm4dtvvwWXy+3zBDwhBKWlpSgvL0dSUhJcXV1hb2+PgQMHYvjw4bjvvvvg5+eH+vp6nDt3DpcuXUJZWRna29v7+tK7oLm5We9AKWOCEom9vb1OIhGLxXjqqafQ1NSEv/76y6zlGRsbGwwaNAjJyclYs2YN4uLi8Pnnn2u8r5+fH+rq6tRu4/F4sLKygqenZ6+Oz2azUVhYiDFjxiAnJwcCgQAsFgtLlizB9evXsXLlSgwaNAi///47XnjhBZw/fx6urq44ffq01vO8nXHXk8nIkSPx008/4fDhw9i6dSvq6upwzz33oKGhQeP9dW2XLegeLBar29U+i8XC4MGD8cYbbyAjIwOFhYWYPHkyduzYgcjISEydOhWbN29GdXV1j4mFEIKSkhJUVVUhOTkZLi4uXe5ja2uLoKAgJCUl4f7778eAAQPQ3NyMCxcu4MKFCygtLYVAIOgzqTU1NSErKwuRkZFmJ5KsrCzY2dnptPeXSCRMs/3w4cNwd3c34ZnqBiEEYrFY499Gjx6No0ePqt125MgRJCcn9ylk7ebNm+jo6MD48ePxn//8B6dOnQKbzcbvv/+OyspKrF+/HgCwdOlSLF++HCtXrsT48eOxcOHCXh+zv+KuL3N1Rnt7O8LDw7FixQosX768y9+NsV22QDdoJktaWhr27t2LCxcuIDk5GSkpKUhJSdGayaL6+KKiIvB4PCZ2uCeQSqVqti52dnaMrUtPrd2bmpqYrHSa420OyGQyZGVlMYFj3RGJTCbDc889h8LCQpw8edKsYgVAc2b72rVrcejQIUycOLFLZjuVBs+fPx8vvvgiLly4gAULFhhMGvzRRx9hz549qKurw4IFC/Cvf/0LP/74I65fv46lS5di8ODBzH3pIKKqzcqdgDvnlRgINICpuLhY49+NsV22QDdYLBaCg4OxbNkyLF26FDU1NYx1/ttvv424uDgmkyUsLEzt4k7z6xsaGpCcnMw0i3sCa2tr+Pv7w9/fH3K5nCGWjIwMxtrd19dXp3CAWrUMHjyYGU4zB2QyGRN8Fhsbq5NI5s+fj4KCgn5BJIByLujpp59Wy2ynRAJ0FeaEhobir7/+wrJly/DVV18hICAAGzdu7DORUGJYsWIFJkyYgAMHDmD16tW4dOkSoqKicOnSJZw5c0aNTOj3404iEsCyM+kCsViM8PBw/Otf/8I777zT5e8rV67EH3/8gatXrzK3LVy4EDk5OZYGvBlACAGfz2eI5eTJkxgyZIha2Nfzzz+PsWPH4sknnzR4pK1cLlezdVH1C3Nzc1O7SFMiMbdVi1wuR1ZWFthsNuLj47ttBMvlcixevBjnz59Henq6WQnQ3Kivr4enp2eXxULnRnxBQQH+/e9/w8rKCidPnoRMJsPly5eRlJRk6lM2Ke76nsmrr76KU6dOoaysDJcuXcIjjzyC1tZWzJ07F0DXOZYFCxagoqICy5cvx7Vr1/Ddd99h27ZtePXVV7Ueo6ezLOnp6V3uz2KxUFhYaLgXfoeAxWLBx8cH8+fPx6FDh1BXV4elS5ciMzMTo0ePRlxcHC5evIiQkBCjWHxwOBx4e3sjOjoaY8eORXR0NBQKBfLz83H69GkUFBQwNiT9hUiys7PBYrF0EolCocCyZctw5swZHDt27K4mkv/9738YNWoUMjIyuvTLVIlEoVAgOjoaaWlpeOaZZzBhwgS4uLiY1c3AVLjrdyZz5szB6dOnUV9fD29vb4waNQr//e9/GXnhs88+i/LycqSnpzOPOXXqFJYtW8Z4/KxcubJbj5/Vq1dj9+7dXWZZtJULqPNxUVGRWpPY29v7jpMTGgtisRgPP/ww8vPzER8fj6NHj2LAgAFM7r2uHkFfQQhBc3MzuFwu6urqIJVK4erqipCQEHh6eprlc5TL5cjJyYFCoUBiYqJOIlmxYgX+/PNPpKenm8y1uL+io6MDw4YNg7OzM7755hsMHz5cazlTdacil8shEAjg6up628+R6MJdTyamQE9nWSyZLH0DIQQPPfQQqqurcejQIXh4eKCtrU0tk8XLywuzZs3C7NmzkZycbDRiqa+vR25uLkJCQiCXy8Hj8SCRSNRsXUxRO5fL5cjNzYVcLkdCQkK3x1QoFHjrrbewa9cupKen3/XBcrRRLpVKkZycDIlEgu+++w4jR47U+3tzO7n/9hZ3fZnLVOjJLAtFQkIC/P39MWHCBJw8edIEZ3lngMVi4eWXX8bRo0cZAz1nZ2c89thj+N///gcul4tPP/0UDQ0NSE1NxZAhQ/Daa6/h3LlzkMvlBjsPamcfHR2N8PBwREZG4t5778Xw4cPh4OCAGzdu4NSpU8jJyUFNTY3R5pSooaZMJtNJJIQQ/Pe//8XOnTtx7Nixu55IAGWjXCaTwdraGjk5OXB0dMTcuXNx/vx5vVNH73QiASw7E5Pg4MGDEAqFiIyMZDKeCwsLUVBQoFEBZslkMR1EIhGOHj3KZLLY2tpi5syZSE1N7XEmiyr4fD5DJN31xwQCAdO8FwgEGlMO+wJKJGKxGImJid2+HkII1q5di6+//honTpzAsGHD+nz82x2qOwrVMtWoUaPA4/Hw/fffY8yYMXd0+UpfWMjEDNA1y6IJlkwW40MikahlshBCMH36dMyePRtjx47V++JOiSQmJqbLgGt3EAqFDLG0trbCzc2NIZbeqNCoEKCjowNJSUk6ieSzzz7Dhg0bcPz4ccTHx/f4eHcaKHm0tbVBLBbD2tpabeJ/zJgxuHnzJn744QeMHTv2ricUC5mYCRMnTsSgQYOwefNmve5vyWQxLWQyGc6cOcNksnR0dGD69OlISUnBhAkTtF7cacBWT4mkM0QiEUMszc3NcHFxYWZZ9PELUygUuHLlCtrb23VOeRNC8MUXX2DdunU4cuQIhg8f3uvzvlNAiaS4uBhz586FUChEbW0tVq9ejSlTpjChZQ888ADKysqwadMmTJo06Y6bHekJLGRiBuiaZdEESyaL+SCXy3H+/Hkmk6W5uRlTpkxBamoqJk6cyAxBHj9+HAqFAnFxcQYN2JJIJAyxNDY2donP7QxCCK5cuQKBQICkpKRud1SEEHzzzTd47733cPDgQZNnkPRnVFRU4J577sHMmTOxcOFCHDhwAB999BEWLlyIefPmITIyEgBwzz33oLKyEleuXLkjrOR7DWKB0fHKK6+Q9PR0cuPGDXLx4kUyY8YM4uzsTMrLywkhhLz++uvk6aefZu6/fv16snfvXnL9+nVy5coV8vrrrxMAJC0trdvjVFVVkSeffJJ4eHgQe3t7EhcXRzIyMrp9THp6OklMTCS2trYkNDSUbN68ue8v+A6GXC4nFy5cIK+88goJDw8njo6OZPbs2WTJkiXE1taW/Pnnn6S9vd1o/zU3N5Pi4mJy9uxZsn//fnL06FGSm5tL6urqiEAgIAKBgFy6dIkcPXqUNDU1dftcAoGAfPHFF8TZ2ZmcOnXK3G9tv4JIJCJPP/00eeaZZ5jbJk6cSMLCwoibmxtZvHgxKSwsZP5WUFBgjtPsV7h792QmRFVVFR5//HG1WZaLFy8yIUiGyGRpamrCvffeiwceeAAHDx6Ej48PSktLu5UWW7JZeg42m41Ro0Zh1KhR+Oijj5CdnY33338fX375JSIiIvD111+jrq4O06ZNg4uLi8FVPNbW1ggICEBAQABjLsrlcvH333/D1tYWHA4HUqkUI0aM0Lkj2b59O1atWoX9+/dbhB2dIJfLMWHCBCaEb+rUqZBKpSgtLcUnn3yC1atXQy6XY+HChRg2bBiGDh16V8h/u4OlzHWH4PXXX8e5c+dw5swZvR9jyWbpO3777Tc8//zz+O233xAcHMxksly/fh3jx49HSkqK0TJZVCGTyZCbm4uWlhYAYPzCqK1LZ6+y//3vf3j55ZeRlpaGyZMnG+28bhdoyiapqqpCYGAg0tLSsG7dOuzYsQMRERH43//+h2XLloHFYuHYsWOIiooy01n3L1jmTO4Q7N+/H8nJyfi///s/+Pj4ICEhAVu3bu32MZZslr5BoVBg69at2L17N6ZPn45hw4bhP//5D/Ly8pCbm4t7770XW7ZsQXh4OGbNmoVt27aBx+P12b6+MwghKC4uRkdHB+655x6MGzcOUVFRDMGcPn0aV65cwf79+yGRSLB3714sXrwYO3fuNBmRrFmzBsOHD4ezszN8fHyQmpqKoqKibh9jKlshmUwGNpsNsViMiooKlJSUAAAGDBgAFouFxsZGKBQKRvjQ3NyMN954AxcvXrQQiSrMWGKzwICwtbUltra2ZNWqVSQrK4t8/fXXxM7Ojvz4449aHxMREUE++OADtdvOnTtHAJCamhpjn/IdAYVCofPvxcXFZO3atWTEiBHEysqK3HfffeTTTz8lxcXFRCAQ9KmHIhAISGZmJjl06BCpr6/v8ve2tjZSWVlJ9u3bR9zd3YmTkxNxcXEhb7zxBhGJRCZ6lwiZPHky+f7778mVK1dITk4OmT59OgkODiYCgUDrY06ePEkAkKKiIlJbW8v8J5PJDHZe9PNra2sjI0eOJAMHDiQDBw4kkyZNIsXFxYQQQnbv3k2cnZ3JokWLyJIlS4iNjQ3Zs2ePwc7hToGFTO4QWFtbk9GjR6vd9vLLL5NRo0ZpfUxERAT58MMP1W47e/YsAUBqa2uNcp53MxQKBSkvLyeffvopGTNmDLGysiKjR48ma9euJdeuXesxsQgEApKVlaWVSDr/t2vXLjJo0CDmQu7i4kKeeOIJUlZWZvL3gsfjEQDdNv4pmTQ1NRnlHCgpKRQKMmvWLJKamkoOHjxI9u/fT+Lj40lYWBjJyckhhBCyZs0acs8995CxY8eSX375xSjnc7vDUua6Q+Dv798l+3rIkCFqjf3OsGSzmBYsFgsDBw7E8uXLcfr0aZSXl+Pxxx/HoUOHMGzYMIwbNw7r16/HjRs3dJbCyD+lLS6Xi6SkJJ2zJ8ePH8ezzz6Ld999F3/88QfKy8tx/PhxBAcHw8nJyZAvUy/Q3g61u+kOxrIV4nA4UCgU+Oqrr+Dh4YF169ZhypQpmDlzJrKzsxEYGIgnnngCgLInefjwYRw4cIC5TddndNfBzGR220Mul+ssdZgCjz/+OBkzZozabUuXLu2yW1HFihUryJAhQ9RuW7BgQbe7GQsMD4VCQWpra8nmzZvJxIkTibW1NYmLiyPvvPMOycrK6rJjEQgEJCcnhxw8eJDw+XydO5KDBw8SJycn8t133/WL76pCoSAzZ87s8n3tjMLCQrJlyxaSmZlJzp8/TxYuXEhYLJZBZcxbt24lXl5exMPDg1RVVRFCCBEKhYQQQm7evEn8/f3Jjh07DHa8OxkWMukh5HI5OXHiBDl9+rS5T0UNf//9N7GysiIffPABKS4uJr/88gtxcHAg27dvZ+7TeZ7lxo0bxMHBgSxbtoxcvXqVbNu2jVhbW5Pdu3eb4yVYQJQX2vr6erJt2zYybdo0YmNjQ6Kjo8mqVavI33//Tdra2shLL71EVq9eTXg8nk4iOXLkCHF2diabN2/uF0RCCCEvvfQSGThwIKmsrOzxY2fMmEFmzpzZ62N3fg9KSkrIO++8Q2xtbcm///1vtfvV1taS8PBw8uuvv/b6eHcTLGTSAxQWFpL777+fxMXFkcGDBxNHR0fy+OOPkwsXLpj71AghhPzxxx8kJiaG2NrakqioKLJlyxa1v8+dO5eMHTtW7bb09HSSkJBAbGxsSEhIiM6hxZ4ORtK6d+f/rl271uvXebdAoVCQpqYm8tNPP5GUlBRib29PwsLCiKurK/nxxx9JW1tbt0Ry8uRJ4uLiQjZu3NhviGTx4sVkwIAB5MaNG716/Pvvv0+ioqJ69djOjXsqQGhsbCTvvvsuCQkJIcuWLSOEENLR0UEuXbpEXFxcyIEDB3p1vLsNFjLpAZ566ikycuRIcuLECSIQCMj58+fJU089RR555BFG+XEno7GxkQwcOJA8++yz5NKlS6SsrIwcO3aMlJSUaH2MKRQ5dwveffdd4uzsTKZNm0acnJxISEgIWbJkCUlPT+9CLGfOnCFubm7kk08+6RdEolAoyKJFi0hAQAC5fv16r5/n4YcfJg888ECPH6f6fVuwYAFJSUkhgwcPJps2bSLl5eWkpaWFvPfee8TJyYkMGTKETJ8+ndxzzz1kxYoVvT7Xuw0WMukBEhISyPPPP692W1lZGfntt98YRQz94crlciKXy019ikbFypUrdda5O8PYipy7BevXryceHh6MukggEJDdu3eTJ554gri6DQDrWAAAFKZJREFUupKgoCCyaNEicvToUXLu3Dni7u5OPvzww35BJIQQsnDhQuLq6krS09PVFhW0P0GI4WyFusPEiRNJcnIy2bFjB/noo4+IlZUVWbVqFSGEED6fTz788EMSGRlJJk+eTEpLSwkhyt/0nfZbNgYsZNIDfPvtt8TR0ZGsX7+etLa26v24/vKD7iuGDBlCli5dSh555BHi7e1N4uPju5TSOoOSSUhICPHz8yPjx48nJ06cMNEZ3zk4f/48ycrK0vg3oVBIfv/9dzJ37lzi6upK2Gw2ee211/rV905TqRMA+f7775n7dC7Drlu3joSHhxM7Ozvi7u5OxowZ06uSE30fduzYQWJjY5nf7ieffEK8vb1JdnY2c9/Gxkaydu1aEh8fT5YuXdrlOSzQDguZ9BCfffYZiYqKIlOmTCGHDh3q8verV6+Sd999lyxevFir6uR2XeX0ZjDSFIocC25BLBb3qx6JqaH6uiUSidrftm7dSlJTUwkhhLz66qvE19eX6Xdev36dHD58mBBCSH19Pfnkk09IbGwsefzxx0105rc/LGTSC5w+fZo88cQTJCQkhPz111/M7V988QUJCQkhDz30EHniiSeIv78/mTdvnlbykMvlt1XvoDeDkZrQV0WOBRZogiqR7N27l2zbtk2tl7lx40YSHR1NPvjgA+Lr60vS09OZv3399dfkiSeeIHV1dYQQQpqamsh7771HlixZYrLzv91hIRM9UFtbS27evNnl9nvvvZc89NBDRKFQkFOnTpGBAweSL7/8kvn79evXydChQ8kPP/xACCGkrq6OfPvttyQ/P99k525IBAcHd+kZbdq0iQQEBPToefqiyLHAAl345JNPyMCBA8krr7xCioqKmNvr6+vJ6NGjCYvFIn/++Sdze0lJCRk8eDB577331J6nvb3dZOd8J8BiQa8HaD44tRkHAKlUisTERBw6dAgsFgtHjx5FU1MTVqxYge3bt2P27NlYsWIFBgwYwEyh5+fn46OPPkJISAiSk5ORkZGBV199FRMnTuxiX63JxdTcuPfee7uY812/fp2x0tcX2dnZ8Pf3N+SpWWABAGDz5s1YvXo1du3ahZEjR8Ld3Z35m7u7O5YtW4Y1a9Zg1apVqKmpQV1dHX7++WcMHz4cb7/9NoBbvz0aemaBfuhfV6t+CpqfPWXKFDz33HPYu3cvFixYgLS0NDz66KMAgIKCAtx///2oqqpCSkoK0tLS4OjoiKNHj6KjowMAUFRUhMrKSggEAkRFRSEsLAzPPvsszpw508WenBKJXC6HQqEw7QvWgmXLluHixYv48MMPUVJSgh07dmDLli1YtGgRc59Vq1bhmWeeYf69YcMG7Nu3D8XFxSgoKMCqVauQlpaGxYsXaz1OSEiIRrdY1eN0xqlTp5CUlAQ7OzuEhYXh66+/NsyLtuC2QXFxMbZs2YLvv/8eU6ZMYYhEKBTi/PnzKCgowP/93/9h27ZtGDJkCD7++GNkZWXh6aefxi+//AJA+Xvrb4u42wbm3hrdTjh+/DiZM2cOiY2NJbNmzSKbNm1iaqyTJk1SkzUSotw+//zzz+TSpUukvb2dPProo2To0KHM30UiERk3bhx55plniFQqJYQoLRy+/PJLsmvXLiIWi9Wej/ZXfv75ZxIVFcUc25To6WBkbxQ5PB5PTT569OhRAoCcPHlS4/3pJP+SJUvI1atXydatWy2T/Hchrl69SiIjI9WGiDdu3EhmzZpFWCwW8fHxIU888QTzt5aWFrXH3079y/4IC5noAU0N9M5fxJ9//pmEhYWRY8eOaXyOc+fOkdjYWLJ27VpCyC2lyZo1a5j+gVgsJn/99Rd54YUXSExMDHF1dSWLFy9mtPj0yz59+nQyceJE0tzcbJgX2M+xZMkSEh4erlWhtGLFii49mPnz51s8xu4yZGdnEycnJ/LJJ5+QjIwMkpqaSpKSkshLL71Ejh07Rr777jvi5+fHLIDoAo4Qi/TXELCQSQ/QWX2l+gWUyWRk0aJFxM/Pj6SmppIvvviCrF27lhl82rRpE7GxsWFsRKiVw6xZs8js2bOZ51H9gh88eJDExMSQnTt3qp2HjY0N2bp1613xAxCLxcTT07NL7ooq7rvvPjVfJUII2bNnD7GysuoiD7XgzsZnn31GWCwWCQwMJAkJCeT48ePMwGxFRQUJDQ0l33zzjXlP8g6FpTjYA7DZbHA4HObfqn0ODoeDL7/8En/++ScGDBiAffv2QSgUwtvbG83Nzbh8+TIAoL6+HgBga2uLtrY2HD16FFOmTAGgTHDbv38/vvrqK2RlZWHKlCmIjY3F77//zhzn999/h7W1NUaOHKk1BvbAgQMoLi42+Os3B/bt24fm5mY8++yzWu9TV1cHX19ftdt8fX2ZjHQL7h4sW7YMhYWFOHToELKysjB+/Hi4ubkBUP5+vby8LPEKRoJFzWUgkH/UWElJSUhKSgIAtLe3w9HREWfPnsWNGzcQHx+PnTt3IjY2FlevXsX69evh7u6OF154AU1NTZg2bRoEAgEGDBiA1atXw83NDXw+Hy+99BJEIhHs7Oywbds23HfffQgODtZ4/Orqanz44YcIDAzE//73P3O8FQbFtm3bMHXqVAQEBHR7v87ESv7JmjBm7roF/RORkZFdbqurq8MTTzwBDw8PPPzww2Y4qzsflp2JgUAvWgqFAnK5HADg6OgIQCkJ5nK5WLNmDQQCATw9PfHII4+goaEBmzdvBpvNxo8//oi6ujp8+eWX2LNnD8rKyrB48WJwOBxERkbCzs4OAHD06FHMnDkTzs7OasenF8/MzEyw2Wxmt0PP5XZERUUFjh07hhdeeKHb+1lCvizQBi6Xi/Xr1+OZZ54Bi8XCoUOHANzev4v+CsvOxMDoLCtsbGzE5cuXMWDAAIwfPx7jx4/HO++8g4KCAkyYMIHRskskEnA4HAQEBDCpeQ0NDXB1dUVUVBQAZfmKxWLhnnvu6XIc+u+LFy+CxWLhvvvu03g+FHK5HCwWq1/LIL///nv4+Phg+vTp3d5v9OjR+OOPP9RuO3LkCJKTk2FtbW3MU7Sgn6OoqAgHDx5ETEwMPvvsMwCATCaDlZXl0mdwmLdlc+fj+vXr5P777ycrV64khJAucl+KqqoqEhsbSxISEsjHH39MHnnkEWJnZ0fmzZtHuFwuIYSQ2bNnkwkTJnRx4KWN+Lq6OjJr1iwyd+5co70eU0Eul5Pg4GDmfVOFJeTLuPjwww9JcnIycXJyIt7e3iQlJYUUFhbqfFx6ejpJTEwktra2JDQ0VGc2jqnA5/OZ/2+R/xoP/XdZeocgIiICp06dwptvvgkAsLa21pgdHRgYiOPHj+ORRx7BjRs3MGnSJMhkMsTHx8PHxwcAcOLECcycORMuLi5qj6XPd/nyZfB4PIwePRoAugw7tre348iRI3j++efx2muvoaCgoMt5EKXCr+8vvI84duwYbt68ieeee67L32pra9Wy7UNDQ/HXX38hPT0d8fHx+O9//4uNGzd2Wxvv6WBkenq6xvsXFhb2/cX2M5w6dQqLFi3CxYsXcfToUchkMkyaNAnt7e1aH1NWVoZp06bhvvvuQ3Z2Nt544w38+9//RlpamgnPXDO8vLwAKL/bqgIaCwwMs1KZBQw6y3zFYjHZu3cvycvLI4QohwVZLFa3qYZvvfUWGTNmDLl69SrznHRGpry8nCxcuJAMHDiQLFu2jEyfPp0EBweTzz//XOvzyWSy29bhWBd6Ohh5N4d88Xg8AqBbp2fLrI8FlsJhPwGLxQIhBAqFAhwOBzY2NkhNTWX+7uLiguXLlyM0NFTj4+vr63HlyhWEhIRgyJAhzHNSbNmyBWfOnMFvv/2GkSNHAgC+/fZbbN68GRMmTEB0dDQuXryI7OxsPProo/D09LyjV3He3t5q/167di3Cw8MxduzYbh/n4+PDSE3vFrS0tAAAPDw8tN7nwoULmDRpktptkydPxrZt2yCVSi29q7sAljJXPwKLxVK7gBOVctP999+PTz75pMsPmpayMjIywOfzMWbMGLXbWSwW6uvrcfLkSXC5XEyaNAkTJ07ETz/9hCeffJIxuwOUDf7//Oc/eO+99/Dwww/jqaeewrVr1zSea3/xCzMEJBIJtm/fjueee06nlDghIQH+/v6YMGECTp48aaIzNB8IIVi+fDnGjBmDmJgYrfezzPpYYCGTfgzVC5s2KSNVY2VmZuL8+fM4cOAAzp8/j+bmZuY+N27cgEgkwurVq3Hp0iUkJiZi7dq1cHNzA5fLZS4CmZmZkEgkcHZ2xtNPP43GxkYsWLAAPB4PgDq50eNqI5XbiWz0GYz09/fHli1bkJaWhj179mDw4MGYMGECTp8+bboTNQMWL16MvLw8/Prrrzrva5n1ucth1iKbBQaDQqEgaWlpZPz48cTKyor4+PiQp59+mly8eJG0tLSQ4OBgsm3bNub+crmcZGVlkV9//ZWIRCJy+fJlEh0dTd555x3mPrm5uYTFYpFz584xt7W2tpLt27czqXSacLvlQEyaNInMmDGjx4+700O+Fi9eTAYMGEBu3Lih874WSxsLLDuTOwQsFgsPPfQQjh8/DqlUii+//BLNzc34/fff4eLigkmTJmHHjh0oLy8HoNxZJCQkYM6cObC1tcVff/0FT09PTJ48mXnOlpYWDBs2jClT5Obm4p577sHbb7+NuXPnIi4uDnV1dUy5h+5GYmNj8cUXX6gNcJaXl6OiosKE74h+0HcwUhNGjRp1x9jWqIIQgsWLF2PPnj04ceKE1j6dKkaPHo2jR4+q3WaZ9bnLYG42s8BwkMlkWtVF5eXlZNy4cSQgIIA8//zz5NNPPyVvvvkm8/fJkyeTefPmqbkh//e//yVjxowheXl5pKGhgdx7773kwQcfJAUFBYQQQj7//HMmuS4/P58IBAKyZs0awmazSUlJidrx33jjDRIdHU1yc3ON8Mp7j3fffZf4+fmpGWzqi4cffpg88MADRjgr82LhwoXE1dWVpKenqynXqHs1IZZZHwu6wkImdyhUZb2qsuPDhw+TefPmkZkzZ5Jvv/2WEKK0x09OTiYbN25k7ieRSEhKSgp54okniFQqJb/++isJCgoiWVlZzH0KCwuJt7c3efjhhwmfzyd5eXmExWIROzs7MnLkSPL9998TQghpa2sjTz75JHnkkUeYi3Z/cDzuyWDk+vXryd69e8n169fJlStXyOuvv04AkLS0NFOeskkAQON/9PMkpGtuDSHKocWEhARiY2NDQkJC+s3QogWmgUUafIeis7sxjSKdNGkSI+GUSqUAgL///hsikQjR0dHMY6g6bOzYsbCyskJWVha8vb2RkJDAmEoOGjQIIpEII0eOhIuLC7y8vBATE4OkpCQEBATgs88+g6enJ4KCglBZWYlp06YxNhZUCm3O5mxPBiMlEgleffVVVFdXw97eHtHR0Thw4ACmTZum8zgymQyrV6/GL7/8grq6Ovj7++PZZ5/FW2+91a2dzalTp7B8+XIUFBQgICAAK1aswIIFC3r3YnsAosfQ6g8//NDltrFjxyIrK8sIZ2TBbQEzk5kFJkbnTBaK8vJy0tHRwfx71apVJCkpiaSnpxNCCHnooYfIww8/TAi5Fex1/PhxYm1tzSTbXbt2jbDZbLWkO0KU5bDhw4eTDz74gLz11ltk48aNaiWTOx3vv/8+8fT0JH/++ScpKysju3btIk5OTmTDhg1aH2NJj7TgdoNlZ3KXQdtKeODAgWr/jo6OhlAoZOz0g4KCcPLkSdTV1cHPzw8tLS3YuHEjkpOTERYWBgDYs2cPgoKCEB0dzew6RCIRLly4gKtXryI6Ohrh4eHYvHkzjh07hh07djDOyncyLly4gJSUFMawMiQkBL/++isyMjK0Pubrr79GcHAwNmzYAAAYMmQIMjIy8Mknn1gs1C3onzA3m1lwe6C6upoMHjyYjB07lnz11VdkypQphMVikXXr1jHmlQkJCWTx4sWEkFtRxxcuXCCJiYlk4cKFzHMdO3aMODs7k0uXLpn+hZgBa9asIQMHDiRFRUWEEEJycnKIj48P2bFjh9bHWKS2FtxusEiDLdAIuVzO1M6bm5sREBCAgwcPIj4+HmVlZZgwYQKcnZ0xevRo2NjYoK2tDTk5OUhJSQFwq+5+/vx52NraYs6cOcxzczgcDB48uF9KhY2BlStX4vHHH0dUVBSsra2RkJCApUuX4vHHH9f6GMtEuQW3GyxlLgs0QrWBf/bsWTQ0NGDu3LnYsGEDmpub8cwzzyA5ORlxcXEAlK6xgYGBzOQ9h8OBWCxGdnY2vL29GT8wQBkWRgjRa37hTsBvv/2G7du3Y8eOHYiOjkZOTg6WLl2KgIAAzJ07V+vjLBPlFtxOsJCJBTphY2ODDz74AO+++y7Gjx+PzMxMVFVVYf/+/YwdflhYGObMmYM5c+Zg7Nix+Omnn1BbW4uamhqMGzcOtra2AACBQICcnBx4enoiOTnZnC/LZHjttdfw+uuvM7uzYcOGoaKiAmvWrNFKJpb0SAtuN1jKXBboxKRJk1BUVISPPvoI1tbWeOyxx3Du3Dnce++9zH2cnJzw8ccfo6KiAqmpqbC1tUVWVhZu3LiBUaNGMfe7evUqSktL1XYqdzqEQmEX4QOHw+nWv8wyUW7BbQfztmwsuJ2hmnWimp1C0d7eTg4fPkxEIhFz26effkri4uLU/L7udMydO5cEBgYy0uA9e/YQLy8vsmLFCuY+lolyC253WMpcFvQaqqttmjxIVAYRHRwc1DIu5HI52Gw2PDw81HYrdzq++OILvP3223jppZfA4/EQEBCA+fPn45133mHuoy09ctmyZfjqq68QEBCgMz3SAgvMCRYh/SCj1YK7CjKZjJmEt8ACC+4MWMjEApOB/JMv352FiAUWWHB7wkImFlhggQUW9BmWJaIFFlhggQV9hoVMLLDAAgss6DMsZGKBBRZYYEGfYSETCyywwAIL+gwLmVhggQUWWNBnWMjEAgsssMCCPsNCJhZYYIEFFvQZFjKxwAILLLCgz7CQiQUWWGCBBX3G/wMWUtnGV5EzZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "\n",
    "show_iris()\n",
    "show_iris(0, 1, 3)\n",
    "show_iris(1, 2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b073794-0308-42cc-b348-650d6d38bcd5",
   "metadata": {},
   "source": [
    "## 距离函数\n",
    "\n",
    "在很多算法中（比如*k-Nearest Neighbors*算法），都需要对项目进行比较，找出它们的*相似度或*接近度。为此，我们有许多不同的函数供我们使用。以下是该模块中实现的函数。\n",
    "\n",
    "### 曼哈顿距离 (`manhattan_distance`)\n",
    "\n",
    "最简单的距离函数之一。它计算两个项目的坐标/特征之间的差异。为了理解它的工作原理，想象一个2D网格，坐标为*x*和*y*。在这个网格中，我们有两个项目，分别位于`(1,2)`和`(3,4)`的方格中。他们两个坐标之间的差是`3-1=2`和`4-2=2`。如果我们把这些加起来，就得到`4`。这意味着要从`(1,2)`到`(3,4)`我们需要四次移动；两次向右，两次向上。该函数对n维网格的工作原理与此类似。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d697d325-01cd-4ebb-94a4-66b9d420efdc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manhattan Distance between (1,2) and (3,4) is 4\n"
     ]
    }
   ],
   "source": [
    "def manhattan_distance(X, Y):\n",
    "    return sum([abs(x - y) for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = manhattan_distance([1,2], [3,4])\n",
    "print(\"Manhattan Distance between (1,2) and (3,4) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f8393e1-37dc-49e5-a218-5fdfcce7476b",
   "metadata": {},
   "source": [
    "### 欧几里得距离 (`euclidean_distance`)\n",
    "\n",
    "可能是最流行的距离函数。它返回两个项目中各个元素之间的平方差值的平方根。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d0f44a7-0f02-498f-b1ce-afdec5208797",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Euclidean Distance between (1,2) and (3,4) is 2.8284271247461903\n"
     ]
    }
   ],
   "source": [
    "def euclidean_distance(X, Y):\n",
    "    return math.sqrt(sum([(x - y)**2 for x, y in zip(X,Y)]))\n",
    "\n",
    "\n",
    "distance = euclidean_distance([1,2], [3,4])\n",
    "print(\"Euclidean Distance between (1,2) and (3,4) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f40b7c9-ae57-4203-9068-ca94456d4da9",
   "metadata": {},
   "source": [
    "### Hamming Distance (`hamming_distance`)\n",
    "\n",
    "这个函数计算两个项目中单个元素之间的差异数。例如，如果我们有两个二进制字符串 \"111 \"和 \"011\"，该函数将返回1，因为这两个字符串只有第一个元素不同。该函数对非二进制字符串也有同样的作用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "631e612b-8c62-4b72-9e50-eb63802a7956",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hamming Distance between 'abc' and 'abb' is 1\n"
     ]
    }
   ],
   "source": [
    "def hamming_distance(X, Y):\n",
    "    return sum(x != y for x, y in zip(X, Y))\n",
    "\n",
    "\n",
    "distance = hamming_distance(['a','b','c'], ['a','b','b'])\n",
    "print(\"Hamming Distance between 'abc' and 'abb' is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde6b656-6f02-4f77-bdf6-16c246913ac7",
   "metadata": {},
   "source": [
    "### 平均布尔误差（`mean_boolean_error`）。\n",
    "\n",
    "为了计算这个距离，我们找到两个项目的所有元素中不同元素的比率。例如，如果两个项目是`(1,2,3)`和`(1,4,5)`，不同/所有元素的比率是2/3，因为它们在三个元素中的两个不同。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7cec4dd5-bca9-4cc6-b6d0-fac128193b2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Boolean Error Distance between (1,2,3) and (1,4,5) is 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "def mean_boolean_error(X, Y):\n",
    "    return mean(int(x != y) for x, y in zip(X, Y))\n",
    "\n",
    "\n",
    "distance = mean_boolean_error([1,2,3], [1,4,5])\n",
    "print(\"Mean Boolean Error Distance between (1,2,3) and (1,4,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caed8d04-3682-4125-bd33-25e41d9778bb",
   "metadata": {},
   "source": [
    "### 平均误差 (`mean_error`)\n",
    "\n",
    "这个函数找出两个项目之间单个元素的平均差。例如，如果两个项目是`(1,0,5)`和`(3,10,5)`，它们的误差距离是`(3-1) + (10-0) + (5-5) = 2 + 10 + 0 = 12`。因此，平均误差距离是`12/3=4'。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d1b62250-6e06-4f11-9382-57f16aab8db4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Error Distance between (1,0,5) and (3,10,5) is 4\n"
     ]
    }
   ],
   "source": [
    "def mean_error(X, Y):\n",
    "    return mean([abs(x - y) for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = mean_error([1,0,5], [3,10,5])\n",
    "print(\"Mean Error Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a0b54f-8eb6-4592-a6fd-57c74f7768f6",
   "metadata": {},
   "source": [
    "### 平均平方误差 (`ms_error`)\n",
    "\n",
    "这与 \"平均误差\"非常相似，但我们不是计算元素之间的差异，而是计算差异的*平方*。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7f4f479-1904-4113-9cb6-c3f82e1af201",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Distance between (1,0,5) and (3,10,5) is 34.666666666666664\n"
     ]
    }
   ],
   "source": [
    "def ms_error(X, Y):\n",
    "    return mean([(x - y)**2 for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = ms_error([1,0,5], [3,10,5])\n",
    "print(\"Mean Square Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad6cd218-d3a5-4335-b9d3-63c0f3828dc1",
   "metadata": {},
   "source": [
    "### 平均平方误差的根（`rms_error`）。\n",
    "\n",
    "这是 \"均方误差\"的平方根。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b3b6c315-a28c-4db7-bd09-75b72176f408",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root of Mean Error Distance between (1,0,5) and (3,10,5) is 5.887840577551898\n"
     ]
    }
   ],
   "source": [
    "def rms_error(X, Y):\n",
    "    return math.sqrt(ms_error(X, Y))\n",
    "\n",
    "\n",
    "distance = rms_error([1,0,5], [3,10,5])\n",
    "print(\"Root of Mean Error Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  }
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